





'''simple docstring'''







from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
					from transformers import (
					    TFBertModel,
					    TFCLIPVisionModel,
					    TFDeiTModel,
					    TFRobertaModel,
					    TFVisionTextDualEncoderModel,
					    TFViTModel,
					    VisionTextDualEncoderConfig,
					)

if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str   ) ->	Tuple:
						if isinstance(SCREAMING_SNAKE_CASE__,			collections.abc.Iterable   ):
												return x
						return (x, x)


@require_tf
class 					__a      :

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     str       ,     __magic_name__							:     Union[str, Any]   )					->       Dict:







													"""simple docstring"""
													pass

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







													"""simple docstring"""
													pass

							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[Any]:







													"""simple docstring"""
													pass

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     str       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     int       ,     __magic_name__							:     Optional[int]=None       ,     **__magic_name__							:     Tuple   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  VisionTextDualEncoderConfig.from_vision_text_configs(__magic_name__       ,     __magic_name__   )

													UpperCAmelCase_			:      Optional[int]     						=  TFVisionTextDualEncoderModel(__magic_name__   )

													UpperCAmelCase_			:      Optional[Any]     						=  model(input_ids=__magic_name__       ,     pixel_values=__magic_name__       ,     attention_mask=__magic_name__   )

													self.assertEqual(output['''text_embeds'''].shape       ,     (input_ids.shape[0], config.projection_dim)   )
													self.assertEqual(output['''image_embeds'''].shape       ,     (pixel_values.shape[0], config.projection_dim)   )

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     Optional[Any]=None       ,     **__magic_name__							:     str   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  self.get_vision_text_model(__magic_name__       ,     __magic_name__   )
													UpperCAmelCase_			:      List[str]     						=  TFVisionTextDualEncoderModel(vision_model=__magic_name__       ,     text_model=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  model(input_ids=__magic_name__       ,     pixel_values=__magic_name__       ,     attention_mask=__magic_name__   )

													self.assertEqual(output['''text_embeds'''].shape       ,     (input_ids.shape[0], model.config.projection_dim)   )
													self.assertEqual(output['''image_embeds'''].shape       ,     (pixel_values.shape[0], model.config.projection_dim)   )

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     Any       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Optional[int]=None       ,     **__magic_name__							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  self.get_vision_text_model(__magic_name__       ,     __magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  {'''vision_model''': vision_model, '''text_model''': text_model}
													UpperCAmelCase_			:      Dict     						=  TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  model(input_ids=__magic_name__       ,     pixel_values=__magic_name__       ,     attention_mask=__magic_name__   )

													self.assertEqual(output['''text_embeds'''].shape       ,     (input_ids.shape[0], model.config.projection_dim)   )
													self.assertEqual(output['''image_embeds'''].shape       ,     (pixel_values.shape[0], model.config.projection_dim)   )

							def     UpperCAmelCase__	(							self							:     List[str]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     int       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     int=None       ,     **__magic_name__							:     Dict   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  self.get_vision_text_model(__magic_name__       ,     __magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  TFVisionTextDualEncoderModel(vision_model=__magic_name__       ,     text_model=__magic_name__   )

													UpperCAmelCase_			:      List[str]     						=  model(input_ids=__magic_name__       ,     pixel_values=__magic_name__       ,     attention_mask=__magic_name__   )
													UpperCAmelCase_			:      str     						=  output[0].numpy()

													with tempfile.TemporaryDirectory() as tmpdirname:
																			model.save_pretrained(__magic_name__   )
																			UpperCAmelCase_			:      List[str]     						=  TFVisionTextDualEncoderModel.from_pretrained(__magic_name__   )

																			UpperCAmelCase_			:      str     						=  model(input_ids=__magic_name__       ,     pixel_values=__magic_name__       ,     attention_mask=__magic_name__   )
																			UpperCAmelCase_			:      int     						=  after_output[0].numpy()
																			UpperCAmelCase_			:      int     						=  np.amax(np.abs(out_a - out_a   )   )
																			self.assertLessEqual(__magic_name__       ,     1E-5   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     List[Any]=None       ,     **__magic_name__							:     List[Any]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      str     						=  self.get_vision_text_model(__magic_name__       ,     __magic_name__   )
													UpperCAmelCase_			:      Optional[Any]     						=  TFVisionTextDualEncoderModel(vision_model=__magic_name__       ,     text_model=__magic_name__   )

													UpperCAmelCase_			:      Any     						=  model(
													    input_ids=__magic_name__       ,     pixel_values=__magic_name__       ,     attention_mask=__magic_name__       ,     output_attentions=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  output.vision_model_output.attentions
													self.assertEqual(len(__magic_name__   )       ,     vision_config.num_hidden_layers   )

													# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
													UpperCAmelCase_			:      Union[str, Any]     						=  to_atuple(vision_model.config.image_size   )
													UpperCAmelCase_			:      List[str]     						=  to_atuple(vision_model.config.patch_size   )
													UpperCAmelCase_			:      str     						=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
													UpperCAmelCase_			:      List[str]     						=  num_patches + 1
													self.assertEqual(vision_attentions[0].shape[-3:]       ,     (vision_config.num_attention_heads, seq_len, seq_len)   )

													UpperCAmelCase_			:      str     						=  output.text_model_output.attentions
													self.assertEqual(len(__magic_name__   )       ,     text_config.num_hidden_layers   )

													self.assertEqual(
													    text_attentions[0].shape[-3:]       ,     (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])       ,     )

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     float   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  np.abs((a - b)   ).max()
													self.assertLessEqual(__magic_name__       ,     __magic_name__       ,     F"""Difference between torch and flax is {diff} (>= {tol})."""   )

							def     UpperCAmelCase__	(							self							:     int   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  self.prepare_config_and_inputs()
													self.check_vision_text_dual_encoder_model(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     List[Any]   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.prepare_config_and_inputs()
													self.check_model_from_pretrained_configs(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.prepare_config_and_inputs()
													self.check_vision_text_dual_encoder_from_pretrained(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  self.prepare_config_and_inputs()
													self.check_save_load(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  self.prepare_config_and_inputs()
													self.check_vision_text_output_attention(**__magic_name__   )



							@slow
							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  self.get_pretrained_model_and_inputs()

													UpperCAmelCase_			:      Optional[Any]     						=  model_a(**__magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  outputs[0].numpy()

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			model_a.save_pretrained(__magic_name__   )
																			UpperCAmelCase_			:      Dict     						=  TFVisionTextDualEncoderModel.from_pretrained(__magic_name__   )

																			UpperCAmelCase_			:      Union[str, Any]     						=  model_a(**__magic_name__   )
																			UpperCAmelCase_			:      str     						=  after_outputs[0].numpy()
																			UpperCAmelCase_			:      Any     						=  np.amax(np.abs(out_a - out_a   )   )
																			self.assertLessEqual(__magic_name__       ,     1E-5   )





@require_tf
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
													    '''hf-internal-testing/tiny-random-vit'''       ,     '''hf-internal-testing/tiny-random-bert'''   )
													UpperCAmelCase_			:      Dict     						=  13
													UpperCAmelCase_			:      Any     						=  floats_tensor(
													    [
													        batch_size,
													        model.vision_model.config.num_channels,
													        model.vision_model.config.image_size,
													        model.vision_model.config.image_size,
													    ]   )
													UpperCAmelCase_			:      Tuple     						=  ids_tensor([batch_size, 4]       ,     model.text_model.config.vocab_size   )
													UpperCAmelCase_			:      Tuple     						=  random_attention_mask([batch_size, 4]   )
													UpperCAmelCase_			:      Tuple     						=  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

													return model, inputs

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     Optional[int]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  TFViTModel(__magic_name__       ,     name='''vision_model'''   )
													UpperCAmelCase_			:      Any     						=  TFBertModel(__magic_name__       ,     name='''text_model'''   )
													return vision_model, text_model



							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  TFViTModelTester(self   )
													UpperCAmelCase_			:      List[str]     						=  TFBertModelTester(self   )
													UpperCAmelCase_			:      Optional[int]     						=  vit_model_tester.prepare_config_and_inputs()
													UpperCAmelCase_			:      List[Any]     						=  bert_model_tester.prepare_config_and_inputs()

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Any     						=  vision_config_and_inputs

													(
													    (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       
													)			:      Dict     						=  text_config_and_inputs

													return {
													    "text_config": text_config,
													    "vision_config": vision_config,
													    "pixel_values": pixel_values,
													    "attention_mask": input_mask,
													    "input_ids": input_ids,
													    "text_token_type_ids": token_type_ids,
													    "text_sequence_labels": sequence_labels,
													    "text_token_labels": token_labels,
													    "text_choice_labels": choice_labels,
													}





@require_tf
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     int   )					->       Tuple:







													"""simple docstring"""
													# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
													# just reinitialize it.
													UpperCAmelCase_			:      List[str]     						=  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
													    '''Rocketknight1/tiny-random-deit-tf'''       ,     '''hf-internal-testing/tiny-random-roberta'''   )
													UpperCAmelCase_			:      Tuple     						=  13
													UpperCAmelCase_			:      Any     						=  floats_tensor(
													    [
													        batch_size,
													        model.vision_model.config.num_channels,
													        model.vision_model.config.image_size,
													        model.vision_model.config.image_size,
													    ]   )
													UpperCAmelCase_			:      Any     						=  ids_tensor([batch_size, 4]       ,     model.text_model.config.vocab_size   )
													UpperCAmelCase_			:      Tuple     						=  random_attention_mask([batch_size, 4]   )
													UpperCAmelCase_			:      Optional[Any]     						=  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

													return model, inputs

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     int       ,     __magic_name__							:     Dict       ,     __magic_name__							:     List[str]=None       ,     **__magic_name__							:     Dict   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  self.get_vision_text_model(__magic_name__       ,     __magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  TFVisionTextDualEncoderModel(vision_model=__magic_name__       ,     text_model=__magic_name__   )

													UpperCAmelCase_			:      Optional[Any]     						=  model(
													    input_ids=__magic_name__       ,     pixel_values=__magic_name__       ,     attention_mask=__magic_name__       ,     output_attentions=__magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  output.vision_model_output.attentions
													self.assertEqual(len(__magic_name__   )       ,     vision_config.num_hidden_layers   )

													# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
													UpperCAmelCase_			:      Dict     						=  to_atuple(vision_model.config.image_size   )
													UpperCAmelCase_			:      str     						=  to_atuple(vision_model.config.patch_size   )
													UpperCAmelCase_			:      int     						=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
													UpperCAmelCase_			:      List[Any]     						=  num_patches + 2
													self.assertEqual(vision_attentions[0].shape[-3:]       ,     (vision_config.num_attention_heads, seq_len, seq_len)   )

													UpperCAmelCase_			:      Dict     						=  output.text_model_output.attentions
													self.assertEqual(len(__magic_name__   )       ,     text_config.num_hidden_layers   )

													self.assertEqual(
													    text_attentions[0].shape[-3:]       ,     (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])       ,     )

							def     UpperCAmelCase__	(							self							:     Tuple       ,     __magic_name__							:     List[str]       ,     __magic_name__							:     Dict   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  TFDeiTModel(__magic_name__       ,     name='''vision_model'''   )
													UpperCAmelCase_			:      Union[str, Any]     						=  TFRobertaModel(__magic_name__       ,     name='''text_model'''   )
													return vision_model, text_model



							def     UpperCAmelCase__	(							self							:     Any   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  TFDeiTModelTester(self   )
													UpperCAmelCase_			:      Optional[Any]     						=  TFRobertaModelTester(self   )
													UpperCAmelCase_			:      List[Any]     						=  vit_model_tester.prepare_config_and_inputs()
													UpperCAmelCase_			:      Optional[int]     						=  bert_model_tester.prepare_config_and_inputs()

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  vision_config_and_inputs

													(
													    (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       
													)			:      Optional[int]     						=  text_config_and_inputs

													return {
													    "text_config": text_config,
													    "vision_config": vision_config,
													    "pixel_values": pixel_values,
													    "attention_mask": input_mask,
													    "input_ids": input_ids,
													    "text_token_type_ids": token_type_ids,
													    "text_sequence_labels": sequence_labels,
													    "text_token_labels": token_labels,
													    "text_choice_labels": choice_labels,
													}





@require_tf
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
													    '''Rocketknight1/tiny-random-clip-tf'''       ,     '''hf-internal-testing/tiny-random-bert'''   )
													UpperCAmelCase_			:      Union[str, Any]     						=  13
													UpperCAmelCase_			:      Optional[int]     						=  floats_tensor(
													    [
													        batch_size,
													        model.vision_model.config.num_channels,
													        model.vision_model.config.image_size,
													        model.vision_model.config.image_size,
													    ]   )
													UpperCAmelCase_			:      Tuple     						=  ids_tensor([batch_size, 4]       ,     model.text_model.config.vocab_size   )
													UpperCAmelCase_			:      List[str]     						=  random_attention_mask([batch_size, 4]   )
													UpperCAmelCase_			:      str     						=  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

													return model, inputs

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[Any]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  TFCLIPVisionModel(__magic_name__       ,     name='''vision_model'''   )
													UpperCAmelCase_			:      str     						=  TFBertModel(__magic_name__       ,     name='''text_model'''   )
													return vision_model, text_model



							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  TFCLIPVisionModelTester(self   )
													UpperCAmelCase_			:      Optional[Any]     						=  TFBertModelTester(self   )
													UpperCAmelCase_			:      Dict     						=  clip_model_tester.prepare_config_and_inputs()
													UpperCAmelCase_			:      Tuple     						=  bert_model_tester.prepare_config_and_inputs()

													UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  vision_config_and_inputs

													(
													    (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       (
													    UpperCAmelCase_
													)						,       
													)			:      Dict     						=  text_config_and_inputs

													return {
													    "text_config": text_config,
													    "vision_config": vision_config,
													    "pixel_values": pixel_values,
													    "attention_mask": input_mask,
													    "input_ids": input_ids,
													    "text_token_type_ids": token_type_ids,
													    "text_sequence_labels": sequence_labels,
													    "text_token_labels": token_labels,
													    "text_choice_labels": choice_labels,
													}





@require_vision
@require_tf
class 					__a      (unittest.TestCase     ):

							@slow
							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  TFVisionTextDualEncoderModel.from_pretrained(
													    '''clip-italian/clip-italian'''       ,     logit_scale_init_value=1.0       ,     from_pt=__magic_name__   )
													UpperCAmelCase_			:      Optional[int]     						=  VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''   )

													UpperCAmelCase_			:      Optional[int]     						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
													UpperCAmelCase_			:      List[Any]     						=  processor(
													    text=['''una foto di un gatto''', '''una foto di un cane''']       ,     images=__magic_name__       ,     padding=__magic_name__       ,     return_tensors='''np'''   )

													UpperCAmelCase_			:      Optional[Any]     						=  model(**__magic_name__   )

													# verify the logits
													self.assertEqual(outputs.logits_per_image.shape       ,     (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])   )
													self.assertEqual(
													    outputs.logits_per_text.shape       ,     (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])       ,     )

													UpperCAmelCase_			:      Dict     						=  np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]   )

													self.assertTrue(np.allclose(outputs.logits_per_image.numpy()       ,     __magic_name__       ,     atol=1E-3   )   )












'''simple docstring'''







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


snake_case_     :       Optional[int]						=  16
snake_case_     :       Tuple						=  32





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Accelerator,			SCREAMING_SNAKE_CASE__      :			int = 16,			SCREAMING_SNAKE_CASE__      :			str = "bert-base-cased"   ) ->	Dict:
						UpperCAmelCase_			:      Dict     						=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      int     						=  load_dataset('''glue''',			'''mrpc'''   )

						def tokenize_function(SCREAMING_SNAKE_CASE__      :			Optional[int]   ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_			:      Union[str, Any]     						=  tokenizer(examples['''sentence1'''],			examples['''sentence2'''],			truncation=SCREAMING_SNAKE_CASE__,			max_length=SCREAMING_SNAKE_CASE__   )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						UpperCAmelCase_			:      Tuple     						=  datasets.map(
						    SCREAMING_SNAKE_CASE__,			batched=SCREAMING_SNAKE_CASE__,			remove_columns=['''idx''', '''sentence1''', '''sentence2'''],			load_from_cache_file=SCREAMING_SNAKE_CASE__   )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						UpperCAmelCase_			:      Optional[Any]     						=  tokenized_datasets.rename_column('''label''',			'''labels'''   )

						def collate_fn(SCREAMING_SNAKE_CASE__      :			str   ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(SCREAMING_SNAKE_CASE__,			padding='''max_length''',			max_length=128,			return_tensors='''pt'''   )
												return tokenizer.pad(SCREAMING_SNAKE_CASE__,			padding='''longest''',			return_tensors='''pt'''   )

						# Instantiate dataloaders.
						UpperCAmelCase_			:      str     						=  DataLoader(
						    tokenized_datasets['''train'''],			shuffle=SCREAMING_SNAKE_CASE__,			collate_fn=SCREAMING_SNAKE_CASE__,			batch_size=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      int     						=  DataLoader(
						    tokenized_datasets['''validation'''],			shuffle=SCREAMING_SNAKE_CASE__,			collate_fn=SCREAMING_SNAKE_CASE__,			batch_size=SCREAMING_SNAKE_CASE__   )

						return train_dataloader, eval_dataloader





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Any   ) ->	Any:
						model.eval()
						UpperCAmelCase_			:      List[str]     						=  0
						for step, batch in enumerate(SCREAMING_SNAKE_CASE__   ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device   )
												with torch.no_grad():
																		UpperCAmelCase_			:      Dict     						=  model(**SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      str     						=  outputs.logits.argmax(dim=-1   )
												# It is slightly faster to call this once, than multiple times
												UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  accelerator.gather(
												    (predictions, batch['''labels'''])   )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(SCREAMING_SNAKE_CASE__   ) - 1:
																								UpperCAmelCase_			:      Tuple     						=  predictions[: len(eval_dataloader.dataset   ) - samples_seen]
																								UpperCAmelCase_			:      int     						=  references[: len(eval_dataloader.dataset   ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=SCREAMING_SNAKE_CASE__,			references=SCREAMING_SNAKE_CASE__,			)

						UpperCAmelCase_			:      List[str]     						=  metric.compute()
						return eval_metric["accuracy"]





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			int   ) ->	Tuple:
						# Initialize accelerator
						UpperCAmelCase_			:      Union[str, Any]     						=  Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase_			:      int     						=  config['''lr''']
						UpperCAmelCase_			:      Optional[int]     						=  int(config['''num_epochs''']   )
						UpperCAmelCase_			:      Optional[int]     						=  int(config['''seed''']   )
						UpperCAmelCase_			:      List[str]     						=  int(config['''batch_size''']   )
						UpperCAmelCase_			:      Optional[int]     						=  args.model_name_or_path

						set_seed(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  get_dataloaders(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_			:      List[Any]     						=  AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			return_dict=SCREAMING_SNAKE_CASE__   )

						# Instantiate optimizer
						UpperCAmelCase_			:      str     						=  (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						UpperCAmelCase_			:      List[str]     						=  optimizer_cls(params=model.parameters(),			lr=SCREAMING_SNAKE_CASE__   )

						if accelerator.state.deepspeed_plugin is not None:
												UpperCAmelCase_			:      List[Any]     						=  accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												UpperCAmelCase_			:      Tuple     						=  1
						UpperCAmelCase_			:      int     						=  (len(SCREAMING_SNAKE_CASE__   ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												UpperCAmelCase_			:      Tuple     						=  get_linear_schedule_with_warmup(
												    optimizer=SCREAMING_SNAKE_CASE__,			num_warmup_steps=0,			num_training_steps=SCREAMING_SNAKE_CASE__,			)
						else:
												UpperCAmelCase_			:      Any     						=  DummyScheduler(SCREAMING_SNAKE_CASE__,			total_num_steps=SCREAMING_SNAKE_CASE__,			warmup_num_steps=0   )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  accelerator.prepare(
						    SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						# We need to keep track of how many total steps we have iterated over
						UpperCAmelCase_			:      Union[str, Any]     						=  0
						# We also need to keep track of the stating epoch so files are named properly
						UpperCAmelCase_			:      Dict     						=  0
						UpperCAmelCase_			:      int     						=  evaluate.load('''glue''',			'''mrpc'''   )
						UpperCAmelCase_			:      Optional[Any]     						=  num_epochs

						if args.partial_train_epoch is not None:
												UpperCAmelCase_			:      List[Any]     						=  args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint   )
												UpperCAmelCase_			:      Tuple     						=  args.resume_from_checkpoint.split('''epoch_'''   )[1]
												UpperCAmelCase_			:      int     						=  ''''''
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												UpperCAmelCase_			:      Union[str, Any]     						=  int(SCREAMING_SNAKE_CASE__   ) + 1
												UpperCAmelCase_			:      Dict     						=  evaluation_loop(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
												accelerator.print('''resumed checkpoint performance:''',			SCREAMING_SNAKE_CASE__   )
												accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''',			lr_scheduler.get_lr()[0]   )
												accelerator.print('''resumed optimizers\'s lr:''',			optimizer.param_groups[0]['''lr''']   )
												with open(os.path.join(args.output_dir,			F"""state_{starting_epoch-1}.json"""   ),			'''r'''   ) as f:
																		UpperCAmelCase_			:      Optional[int]     						=  json.load(SCREAMING_SNAKE_CASE__   )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						UpperCAmelCase_			:      int     						=  {}
						for epoch in range(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ):
												model.train()
												for step, batch in enumerate(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      Optional[int]     						=  model(**SCREAMING_SNAKE_CASE__   )
																		UpperCAmelCase_			:      Any     						=  outputs.loss
																		UpperCAmelCase_			:      Tuple     						=  loss / gradient_accumulation_steps
																		accelerator.backward(SCREAMING_SNAKE_CASE__   )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												UpperCAmelCase_			:      Tuple     						=  F"""epoch_{epoch}"""
												UpperCAmelCase_			:      Optional[int]     						=  os.path.join(args.output_dir,			SCREAMING_SNAKE_CASE__   )
												accelerator.save_state(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      int     						=  evaluation_loop(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Optional[Any]     						=  accuracy
												UpperCAmelCase_			:      Any     						=  lr_scheduler.get_lr()[0]
												UpperCAmelCase_			:      List[str]     						=  optimizer.param_groups[0]['''lr''']
												UpperCAmelCase_			:      Tuple     						=  epoch
												UpperCAmelCase_			:      Dict     						=  overall_step
												accelerator.print(F"""epoch {epoch}:""",			SCREAMING_SNAKE_CASE__   )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir,			F"""state_{epoch}.json"""   ),			'''w'''   ) as f:
																								json.dump(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  ) ->	List[str]:
						UpperCAmelCase_			:      Optional[int]     						=  argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''   )
						parser.add_argument(
						    '''--model_name_or_path''',			type=SCREAMING_SNAKE_CASE__,			default='''bert-base-cased''',			help='''Path to pretrained model or model identifier from huggingface.co/models.''',			required=SCREAMING_SNAKE_CASE__,			)
						parser.add_argument(
						    '''--output_dir''',			type=SCREAMING_SNAKE_CASE__,			default='''.''',			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''',			)
						parser.add_argument(
						    '''--resume_from_checkpoint''',			type=SCREAMING_SNAKE_CASE__,			default=SCREAMING_SNAKE_CASE__,			help='''If the training should continue from a checkpoint folder.''',			)
						parser.add_argument(
						    '''--partial_train_epoch''',			type=SCREAMING_SNAKE_CASE__,			default=SCREAMING_SNAKE_CASE__,			help='''If passed, the training will stop after this number of epochs.''',			)
						parser.add_argument(
						    '''--num_epochs''',			type=SCREAMING_SNAKE_CASE__,			default=2,			help='''Number of train epochs.''',			)
						UpperCAmelCase_			:      Optional[int]     						=  parser.parse_args()
						UpperCAmelCase_			:      List[Any]     						=  {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

						training_function(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					main()





