from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    AutoTokenizer,
						    TFAutoModelForCausalLM,
						    TFAutoModelForSeqaSeqLM,
						    TFAutoModelForSpeechSeqaSeq,
						    TFAutoModelForVisionaSeq,
						    TFBartForConditionalGeneration,
						    TFLogitsProcessorList,
						    TFMinLengthLogitsProcessor,
						    tf_top_k_top_p_filtering,
						)

if is_tensorflow_text_available():
						import tensorflow_text as text

@require_tf
class       snake_case					(      unittest.TestCase						):




		def 	__lowercase(      self     :	Any				)->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		tf.convert_to_tensor(
							    [
							        [
							            8.222_0991,  # 3rd highest value; idx. 0
							            -0.562_0044,
							            5.2322_9752,
							            4.038_6393,
							            -6.879_8378,
							            -0.5478_5802,
							            -3.201_2153,
							            2.9277_7176,
							            1.8817_1953,
							            7.3534_1276,  # 5th highest value; idx. 9
							            8.4320_7833,  # 2nd highest value; idx. 10
							            -9.8571_1836,
							            -5.9620_9236,
							            -1.1303_9161,
							            -7.111_5294,
							            -0.836_9633,
							            -5.318_6408,
							            7.0642_7407,
							            0.8136_9344,
							            -0.8202_3817,
							            -5.917_9796,
							            0.5881_3443,
							            -6.9977_8438,
							            4.7155_1189,
							            -0.1877_1637,
							            7.4402_0759,  # 4th highest value; idx. 25
							            9.3845_0987,  # 1st highest value; idx. 26
							            2.1266_2941,
							            -9.3256_2038,
							            2.3565_2522,
							        ],  # cummulative prob of 5 highest values <= 0.6
							        [
							            0.5842_5518,
							            4.5313_9238,
							            -5.5751_0464,
							            -6.2803_0699,
							            -7.1952_9503,
							            -4.0212_2551,
							            1.3933_7037,
							            -6.0670_7057,
							            1.5948_0517,
							            -9.64_3119,
							            0.0390_7799,
							            0.6723_1762,
							            -8.8820_6726,
							            6.2711_5922,  # 4th highest value; idx. 13
							            2.2852_0723,
							            4.8276_7506,
							            4.3042_1368,
							            8.827_5313,  # 2nd highest value; idx. 17
							            5.4402_9958,  # 5th highest value; idx. 18
							            -4.473_5794,
							            7.3857_9536,  # 3rd highest value; idx. 20
							            -2.9105_1663,
							            2.6194_6077,
							            -2.567_4762,
							            -9.4895_9302,
							            -4.0292_2645,
							            -1.3541_6918,
							            9.6770_2323,  # 1st highest value; idx. 27
							            -5.8947_8553,
							            1.8537_0467,
							        ],  # cummulative prob of 5 highest values <= 0.6
							    ]     , dtype=tf.floataa     , )

							SCREAMING_SNAKE_CASE__				:				Dict			=		tf.convert_to_tensor(
							    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]     , dtype=tf.intaa     , )  # expected non filtered idx as noted above

							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tf.convert_to_tensor(
							    [8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023]     , dtype=tf.floataa     , )  # expected non filtered values as noted above

							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tf_top_k_top_p_filtering(_SCREAMING_SNAKE_CASE     , top_k=10     , top_p=0.6     , min_tokens_to_keep=4				)

							SCREAMING_SNAKE_CASE__				:				Any			=		output[output != -float('inf'				)]
							SCREAMING_SNAKE_CASE__				:				int			=		tf.cast(
							    tf.where(tf.not_equal(_SCREAMING_SNAKE_CASE     , tf.constant(-float('inf'				)     , dtype=tf.floataa				)				)				)     , dtype=tf.intaa     , )

							tf.debugging.assert_near(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , rtol=1e-1_2				)
							tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE				)




@require_tf
class       snake_case					(      unittest.TestCase   ,						UpperCamelCase_						):
		# setting framework_dependent_parameters needs to be gated, just like its contents' imports
		if is_tf_available():
				lowercase_			=     {
				    'AutoModelForCausalLM': TFAutoModelForCausalLM,
				    'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
				    'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
				    'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
				    'LogitsProcessorList': TFLogitsProcessorList,
				    'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
				    'create_tensor_fn': tf.convert_to_tensor,
				    'floats_tensor': floats_tensor,
				    'return_tensors': 'tf',
				}




		@slow
		def 	__lowercase(      self     :	Dict				)->       int:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
							SCREAMING_SNAKE_CASE__				:				Dict			=		2
							SCREAMING_SNAKE_CASE__				:				int			=		2

							class       snake_case					(      tf.Module						):




									def __init__(      self     :	List[str]     , a_     :	List[Any]				)->       Any:




														"""simple docstring"""
														super(_SCREAMING_SNAKE_CASE     , self				).__init__()
														SCREAMING_SNAKE_CASE__				:				str			=		model




									@tf.function(
									    input_signature=(
									        tf.TensorSpec((None, input_length)     , tf.intaa     , name='input_ids'				),
									        tf.TensorSpec((None, input_length)     , tf.intaa     , name='attention_mask'				),
									    )     , jit_compile=_SCREAMING_SNAKE_CASE     , )
									def 	__lowercase(      self     :	List[Any]     , a_     :	Optional[Any]     , a_     :	List[str]				)->       Optional[Any]:




														"""simple docstring"""
														SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model.generate(
														    input_ids=_SCREAMING_SNAKE_CASE     , attention_mask=_SCREAMING_SNAKE_CASE     , max_new_tokens=_SCREAMING_SNAKE_CASE     , return_dict_in_generate=_SCREAMING_SNAKE_CASE     , )
														return {"sequences": outputs["sequences"]}




							SCREAMING_SNAKE_CASE__				:				Any			=		[[2, 0], [102, 103]]
							SCREAMING_SNAKE_CASE__				:				int			=		[[1, 0], [1, 1]]
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		DummyModel(model=_SCREAMING_SNAKE_CASE				)
							with tempfile.TemporaryDirectory() as tmp_dir:
												tf.saved_model.save(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , signatures={'serving_default': dummy_model.serving}				)
												SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tf.saved_model.load(_SCREAMING_SNAKE_CASE				).signatures['serving_default']
												for batch_size in range(1     , len(_SCREAMING_SNAKE_CASE				) + 1				):
																	SCREAMING_SNAKE_CASE__				:				List[str]			=		{
																	    'input_ids': tf.constant(dummy_input_ids[:batch_size]				),
																	    'attention_mask': tf.constant(dummy_attention_masks[:batch_size]				),
																	}
																	SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		serving_func(**_SCREAMING_SNAKE_CASE				)['sequences']
																	SCREAMING_SNAKE_CASE__				:				Any			=		test_model.generate(**_SCREAMING_SNAKE_CASE     , max_new_tokens=_SCREAMING_SNAKE_CASE				)
																	tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE				)




		@slow
		def 	__lowercase(      self     :	List[str]				)->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Any			=		TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
							SCREAMING_SNAKE_CASE__				:				Any			=		1
							SCREAMING_SNAKE_CASE__				:				str			=		2

							class       snake_case					(      tf.Module						):




									def __init__(      self     :	List[Any]     , a_     :	int				)->       Dict:




														"""simple docstring"""
														super(_SCREAMING_SNAKE_CASE     , self				).__init__()
														SCREAMING_SNAKE_CASE__				:				List[Any]			=		model




									@tf.function(
									    input_signature=(
									        tf.TensorSpec((batch_size, None)     , tf.intaa     , name='input_ids'				),
									        tf.TensorSpec((batch_size, None)     , tf.intaa     , name='attention_mask'				),
									    )     , jit_compile=_SCREAMING_SNAKE_CASE     , )
									def 	__lowercase(      self     :	Any     , a_     :	Dict     , a_     :	int				)->       List[Any]:




														"""simple docstring"""
														SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.model.generate(
														    input_ids=_SCREAMING_SNAKE_CASE     , attention_mask=_SCREAMING_SNAKE_CASE     , max_new_tokens=_SCREAMING_SNAKE_CASE     , return_dict_in_generate=_SCREAMING_SNAKE_CASE     , )
														return {"sequences": outputs["sequences"]}




							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[[2], [102, 103]]
							SCREAMING_SNAKE_CASE__				:				str			=		[[1], [1, 1]]
							SCREAMING_SNAKE_CASE__				:				List[str]			=		DummyModel(model=_SCREAMING_SNAKE_CASE				)
							with tempfile.TemporaryDirectory() as tmp_dir:
												tf.saved_model.save(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , signatures={'serving_default': dummy_model.serving}				)
												SCREAMING_SNAKE_CASE__				:				Dict			=		tf.saved_model.load(_SCREAMING_SNAKE_CASE				).signatures['serving_default']
												for input_row in range(len(_SCREAMING_SNAKE_CASE				)				):
																	SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{
																	    'input_ids': tf.constant([dummy_input_ids[input_row]]				),
																	    'attention_mask': tf.constant([dummy_attention_masks[input_row]]				),
																	}
																	SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		serving_func(**_SCREAMING_SNAKE_CASE				)['sequences']
																	SCREAMING_SNAKE_CASE__				:				Optional[int]			=		test_model.generate(**_SCREAMING_SNAKE_CASE     , max_new_tokens=_SCREAMING_SNAKE_CASE				)
																	tf.debugging.assert_equal(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE				)




		@slow
		@require_tensorflow_text
		def 	__lowercase(      self     :	int				)->       int:




							"""simple docstring"""






							with tempfile.TemporaryDirectory() as tmp_dir:
												# file needed to load the TF tokenizer
												hf_hub_download(repo_id='google/flan-t5-small'     , filename='spiece.model'     , local_dir=_SCREAMING_SNAKE_CASE				)

												class       snake_case					(      tf.keras.layers.Layer						):




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




																			"""simple docstring"""
																			super().__init__()
																			SCREAMING_SNAKE_CASE__				:				int			=		text.SentencepieceTokenizer(
																			    model=tf.io.gfile.GFile(os.path.join(_SCREAMING_SNAKE_CASE     , 'spiece.model'				)     , 'rb'				).read()				)
																			SCREAMING_SNAKE_CASE__				:				Tuple			=		TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5'				)




														def 	__lowercase(      self     :	Dict     , a_     :	Optional[Any]     , *a_     :	int     , **a_     :	str				)->       Any:




																			"""simple docstring"""
																			SCREAMING_SNAKE_CASE__				:				str			=		self.tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
																			SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[str]			=		text.pad_model_inputs(
																			    _SCREAMING_SNAKE_CASE     , max_seq_length=64     , pad_value=self.model.config.pad_token_id				)
																			SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.model.generate(input_ids=_SCREAMING_SNAKE_CASE     , attention_mask=_SCREAMING_SNAKE_CASE				)
																			return self.tokenizer.detokenize(_SCREAMING_SNAKE_CASE				)




												SCREAMING_SNAKE_CASE__				:				Optional[int]			=		CompleteSentenceTransformer()
												SCREAMING_SNAKE_CASE__				:				Dict			=		tf.keras.layers.Input(shape=(1,)     , dtype=tf.string     , name='inputs'				)
												SCREAMING_SNAKE_CASE__				:				List[str]			=		complete_model(_SCREAMING_SNAKE_CASE				)
												SCREAMING_SNAKE_CASE__				:				Dict			=		tf.keras.Model(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE				)
												keras_model.save(_SCREAMING_SNAKE_CASE				)




		def 	__lowercase(      self     :	List[Any]				)->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		{
							    'do_sample': True,
							    'num_beams': 1,
							    'top_p': 0.7,
							    'top_k': 10,
							    'temperature': 0.7,
							}
							SCREAMING_SNAKE_CASE__				:				Tuple			=		14

							SCREAMING_SNAKE_CASE__				:				List[Any]			=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
							SCREAMING_SNAKE_CASE__				:				str			=		'Hello, my dog is cute and'
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer(_SCREAMING_SNAKE_CASE     , return_tensors='tf'				)
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)

							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		638
							# forces the generation to happen on CPU, to avoid GPU-related quirks
							with tf.device(':/CPU:0'				):
												tf.random.set_seed(0				)
												SCREAMING_SNAKE_CASE__				:				int			=		model.generate(**_SCREAMING_SNAKE_CASE     , eos_token_id=_SCREAMING_SNAKE_CASE     , **_SCREAMING_SNAKE_CASE				)
							self.assertTrue(expectation == len(generated_tokens[0]				)				)

							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[638, 198]
							with tf.device(':/CPU:0'				):
												tf.random.set_seed(0				)
												SCREAMING_SNAKE_CASE__				:				List[str]			=		model.generate(**_SCREAMING_SNAKE_CASE     , eos_token_id=_SCREAMING_SNAKE_CASE     , **_SCREAMING_SNAKE_CASE				)
							self.assertTrue(expectation == len(generated_tokens[0]				)				)




		def 	__lowercase(      self     :	List[Any]				)->       List[Any]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart'				)
							SCREAMING_SNAKE_CASE__				:				str			=		'Hugging Face is a technology company based in New York and Paris.'
							SCREAMING_SNAKE_CASE__				:				str			=		bart_tokenizer(_SCREAMING_SNAKE_CASE     , return_tensors='tf'				).input_ids
							SCREAMING_SNAKE_CASE__				:				str			=		TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart'				)
							SCREAMING_SNAKE_CASE__				:				Any			=		bart_model.generate(_SCREAMING_SNAKE_CASE				).numpy()

							class       snake_case					(      UpperCamelCase_						):




									def 	__lowercase(      self     :	Union[str, Any]     , a_     :	str     , a_     :	Optional[Any]=None     , **a_     :	Optional[int]				)->       List[str]:




														"""simple docstring"""
														return super().call(_SCREAMING_SNAKE_CASE     , **_SCREAMING_SNAKE_CASE				)




							SCREAMING_SNAKE_CASE__				:				List[str]			=		FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart'				)
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		bart_model.generate(_SCREAMING_SNAKE_CASE     , foo='bar'				).numpy()
							self.assertTrue(np.array_equal(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE				)				)

							class       snake_case					(      bart_model.model.encoder.__class__						):




									def 	__lowercase(      self     :	str     , a_     :	Union[str, Any]     , **a_     :	Optional[int]				)->       Optional[int]:




														"""simple docstring"""
														return super().call(_SCREAMING_SNAKE_CASE     , **_SCREAMING_SNAKE_CASE				)




							SCREAMING_SNAKE_CASE__				:				str			=		FakeEncoder(bart_model.config     , bart_model.model.shared				)
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		fake_encoder

							# Normal generation still works (the output will be different because the encoder weights are different)
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		bart_model.generate(_SCREAMING_SNAKE_CASE				).numpy()
							with self.assertRaises(_SCREAMING_SNAKE_CASE				):
												# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
												bart_model.generate(_SCREAMING_SNAKE_CASE     , foo='bar'				)




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__		:					int						=					logging.get_logger(__name__)







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Union[str, Any]=False     ,  lowercase__  :			str=False     ,  lowercase__  :			Dict=False ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[]
     for i in range(config.num_hidden_layers ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
          rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
          rename_keys.append(
              (f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
          rename_keys.append(
              (f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
          rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
          rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
          rename_keys.append(
              (f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
          rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
          rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''') )
          rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''') )

     # embeddings
     rename_keys.extend(
         [
             # text embeddings
             ('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
             (
                 'text_embeddings.position_embeddings.weight',
                 'vilt.embeddings.text_embeddings.position_embeddings.weight',
             ),
             ('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
             (
                 'text_embeddings.token_type_embeddings.weight',
                 'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
             ),
             ('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
             ('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
             # patch embeddings
             ('transformer.cls_token', 'vilt.embeddings.cls_token'),
             ('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
             ('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
             ('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
             # token type embeddings
             ('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
         ] )

     # final layernorm + pooler
     rename_keys.extend(
         [
             ('transformer.norm.weight', 'vilt.layernorm.weight'),
             ('transformer.norm.bias', 'vilt.layernorm.bias'),
             ('pooler.dense.weight', 'vilt.pooler.dense.weight'),
             ('pooler.dense.bias', 'vilt.pooler.dense.bias'),
         ] )

     # classifier head(s)
     if vqa_model:
          # classification head
          rename_keys.extend(
              [
                  ('vqa_classifier.0.weight', 'classifier.0.weight'),
                  ('vqa_classifier.0.bias', 'classifier.0.bias'),
                  ('vqa_classifier.1.weight', 'classifier.1.weight'),
                  ('vqa_classifier.1.bias', 'classifier.1.bias'),
                  ('vqa_classifier.3.weight', 'classifier.3.weight'),
                  ('vqa_classifier.3.bias', 'classifier.3.bias'),
              ] )
     elif nlvr_model:
          # classification head
          rename_keys.extend(
              [
                  ('nlvr2_classifier.0.weight', 'classifier.0.weight'),
                  ('nlvr2_classifier.0.bias', 'classifier.0.bias'),
                  ('nlvr2_classifier.1.weight', 'classifier.1.weight'),
                  ('nlvr2_classifier.1.bias', 'classifier.1.bias'),
                  ('nlvr2_classifier.3.weight', 'classifier.3.weight'),
                  ('nlvr2_classifier.3.bias', 'classifier.3.bias'),
              ] )
     else:
          pass

     return rename_keys







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     for i in range(config.num_hidden_layers ):
          SCREAMING_SNAKE_CASE__				:				Dict			=		'vilt.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight''' )
          SCREAMING_SNAKE_CASE__				:				Any			=		state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias''' )
          # next, add query, keys and values (in that order) to the state dict
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		in_proj_weight[
              : config.hidden_size, :
          ]
          SCREAMING_SNAKE_CASE__				:				List[str]			=		in_proj_bias[: config.hidden_size]
          SCREAMING_SNAKE_CASE__				:				int			=		in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          SCREAMING_SNAKE_CASE__				:				List[str]			=		in_proj_weight[
              -config.hidden_size :, :
          ]
          SCREAMING_SNAKE_CASE__				:				Tuple			=		in_proj_bias[-config.hidden_size :]







def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(lowercase__     ,  lowercase__ )







def     _a				(      lowercase__  :			int     ,  lowercase__  :			int     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		dct.pop(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Any			=		val







@torch.no_grad()
def     _a				(      lowercase__  :			Dict     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[Any]			=		ViltConfig(image_size=3_84     ,  patch_size=32     ,  tie_word_embeddings=lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[str]			=		False
     SCREAMING_SNAKE_CASE__				:				Tuple			=		False
     SCREAMING_SNAKE_CASE__				:				List[str]			=		False
     SCREAMING_SNAKE_CASE__				:				str			=		False
     if "vqa" in checkpoint_url:
          SCREAMING_SNAKE_CASE__				:				Any			=		True
          SCREAMING_SNAKE_CASE__				:				str			=		31_29
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'huggingface/label-files'
          SCREAMING_SNAKE_CASE__				:				int			=		'vqa2-id2label.json'
          SCREAMING_SNAKE_CASE__				:				str			=		json.load(open(hf_hub_download(lowercase__     ,  lowercase__     ,  repo_type='dataset' )     ,  'r' ) )
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{int(lowercase__ ): v for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE__				:				Dict			=		idalabel
          SCREAMING_SNAKE_CASE__				:				str			=		{v: k for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE__				:				List[str]			=		ViltForQuestionAnswering(lowercase__ )
     elif "nlvr" in checkpoint_url:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		True
          SCREAMING_SNAKE_CASE__				:				List[str]			=		2
          SCREAMING_SNAKE_CASE__				:				Dict			=		{0: 'False', 1: 'True'}
          SCREAMING_SNAKE_CASE__				:				Dict			=		{v: k for k, v in config.idalabel.items()}
          SCREAMING_SNAKE_CASE__				:				Tuple			=		3
          SCREAMING_SNAKE_CASE__				:				int			=		ViltForImagesAndTextClassification(lowercase__ )
     elif "irtr" in checkpoint_url:
          SCREAMING_SNAKE_CASE__				:				Dict			=		True
          SCREAMING_SNAKE_CASE__				:				str			=		ViltForImageAndTextRetrieval(lowercase__ )
     elif "mlm_itm" in checkpoint_url:
          SCREAMING_SNAKE_CASE__				:				int			=		True
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		ViltForMaskedLM(lowercase__ )
     else:
          raise ValueError('Unknown model type' )

     # load state_dict of original model, remove and rename some keys
     SCREAMING_SNAKE_CASE__				:				Any			=		torch.hub.load_state_dict_from_url(lowercase__     ,  map_location='cpu' )['state_dict']
     SCREAMING_SNAKE_CASE__				:				Any			=		create_rename_keys(lowercase__     ,  lowercase__     ,  lowercase__     ,  lowercase__ )
     for src, dest in rename_keys:
          rename_key(lowercase__     ,  lowercase__     ,  lowercase__ )
     read_in_q_k_v(lowercase__     ,  lowercase__ )
     if mlm_model or irtr_model:
          SCREAMING_SNAKE_CASE__				:				Any			=		['itm_score.fc.weight', 'itm_score.fc.bias']
          for k in ignore_keys:
               state_dict.pop(lowercase__     ,  lowercase__ )

    # load state dict into HuggingFace model
     model.eval()
     if mlm_model:
          SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model.load_state_dict(lowercase__     ,  strict=lowercase__ )
          assert missing_keys == ["mlm_score.decoder.bias"]
     else:
          model.load_state_dict(lowercase__ )

     # Define processor
     SCREAMING_SNAKE_CASE__				:				str			=		ViltImageProcessor(size=3_84 )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		BertTokenizer.from_pretrained('bert-base-uncased' )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		ViltProcessor(lowercase__     ,  lowercase__ )

     # Forward pass on example inputs (image + text)
     if nlvr_model:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'     ,  stream=lowercase__ ).raw )
          SCREAMING_SNAKE_CASE__				:				Any			=		Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'     ,  stream=lowercase__ ).raw )
          SCREAMING_SNAKE_CASE__				:				Tuple			=		(
              'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
              ' standing.'
          )
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		processor(lowercase__     ,  lowercase__     ,  return_tensors='pt' )
          SCREAMING_SNAKE_CASE__				:				List[str]			=		processor(lowercase__     ,  lowercase__     ,  return_tensors='pt' )
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(
              input_ids=encoding_a.input_ids     ,  pixel_values=encoding_a.pixel_values     ,  pixel_values_a=encoding_a.pixel_values     ,  )
     else:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg'     ,  stream=lowercase__ ).raw )
          if mlm_model:
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'a bunch of [MASK] laying on a [MASK].'
          else:
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'How many cats are there?'
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		processor(lowercase__     ,  lowercase__     ,  return_tensors='pt' )
          SCREAMING_SNAKE_CASE__				:				str			=		model(**lowercase__ )

     # Verify outputs
     if mlm_model:
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.Size([1, 11, 3_05_22] )
          SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([-12.5061, -12.5123, -12.5174] )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]     ,  lowercase__     ,  atol=1E-4 )

          # verify masked token prediction equals "cats"
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		outputs.logits[0, 4, :].argmax(-1 ).item()
          assert tokenizer.decode([predicted_id] ) == "cats"
     elif vqa_model:
          SCREAMING_SNAKE_CASE__				:				str			=		torch.Size([1, 31_29] )
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([-15.9495, -18.1472, -10.3041] )
          assert torch.allclose(outputs.logits[0, :3]     ,  lowercase__     ,  atol=1E-4 )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]     ,  lowercase__     ,  atol=1E-4 )

          # verify vqa prediction equals "2"
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		outputs.logits.argmax(-1 ).item()
          assert model.config.idalabel[predicted_idx] == "2"
     elif nlvr_model:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.Size([1, 2] )
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([-2.8721, 2.1291] )
          assert torch.allclose(outputs.logits[0, :3]     ,  lowercase__     ,  atol=1E-4 )
          assert outputs.logits.shape == expected_shape

     Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
     print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
     model.save_pretrained(lowercase__ )
     processor.save_pretrained(lowercase__ )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					str						=					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--checkpoint_url",
          default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
          type=str,
          help="URL of the checkpoint you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )

      SCREAMING_SNAKE_CASE__		:					List[Any]						=					parser.parse_args()
      convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



