


import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase : Optional[int] 			=				logging.get_logger(__name__)

__UpperCAmelCase : int 			=				{
    "b0": efficientnet.EfficientNetBa,
    "b1": efficientnet.EfficientNetBa,
    "b2": efficientnet.EfficientNetBa,
    "b3": efficientnet.EfficientNetBa,
    "b4": efficientnet.EfficientNetBa,
    "b5": efficientnet.EfficientNetBa,
    "b6": efficientnet.EfficientNetBa,
    "b7": efficientnet.EfficientNetBa,
}

__UpperCAmelCase : List[Any] 			=				{
    "b0": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 224,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 240,
        "dropout_rate": 0.2,
        "dw_padding": [16],
    },
    "b2": {
        "hidden_dim": 1408,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 260,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 16],
    },
    "b3": {
        "hidden_dim": 1536,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 300,
        "dropout_rate": 0.3,
        "dw_padding": [5, 18],
    },
    "b4": {
        "hidden_dim": 1792,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 380,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 2048,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 456,
        "dropout_rate": 0.4,
        "dw_padding": [13, 27],
    },
    "b6": {
        "hidden_dim": 2304,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 528,
        "dropout_rate": 0.5,
        "dw_padding": [31],
    },
    "b7": {
        "hidden_dim": 2560,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 600,
        "dropout_rate": 0.5,
        "dw_padding": [18],
    },
}


def      a	(							SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		EfficientNetConfig()
       UpperCamelCase    :	Optional[int]   	=		CONFIG_MAP[model_name]['''hidden_dim''']
       UpperCamelCase    :	List[str]   	=		CONFIG_MAP[model_name]['''width_coef''']
       UpperCamelCase    :	Optional[Any]   	=		CONFIG_MAP[model_name]['''depth_coef''']
       UpperCamelCase    :	int   	=		CONFIG_MAP[model_name]['''image_size''']
       UpperCamelCase    :	List[Any]   	=		CONFIG_MAP[model_name]['''dropout_rate''']
       UpperCamelCase    :	Any   	=		CONFIG_MAP[model_name]['''dw_padding''']

       UpperCamelCase    :	Optional[Any]   	=		'''huggingface/label-files'''
       UpperCamelCase    :	Optional[Any]   	=		'''imagenet-1k-id2label.json'''
       UpperCamelCase    :	Optional[Any]   	=		1_0_0_0
       UpperCamelCase    :	List[str]   	=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			repo_type='''dataset'''       )							,			'''r'''       )       )
       UpperCamelCase    :	Optional[int]   	=		{int(SCREAMING_SNAKE_CASE_       ): v for k, v in idalabel.items()}

       UpperCamelCase    :	Optional[int]   	=		idalabel
       UpperCamelCase    :	Union[str, Any]   	=		{v: k for k, v in idalabel.items()}
       return config


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	str   	=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       UpperCamelCase    :	Dict   	=		Image.open(requests.get(SCREAMING_SNAKE_CASE_							,			stream=SCREAMING_SNAKE_CASE_       ).raw       )
       return im


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	Dict   	=		CONFIG_MAP[model_name]['''image_size''']
       UpperCamelCase    :	Union[str, Any]   	=		EfficientNetImageProcessor(
           size={'''height''': size, '''width''': size}							,			image_mean=[0.485, 0.456, 0.406]							,			image_std=[0.47853944, 0.4732864, 0.47434163]							,			do_center_crop=SCREAMING_SNAKE_CASE_							,			)
       return preprocessor


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		[v.split('''_'''       )[0].split('''block'''       )[1] for v in original_param_names if v.startswith('''block'''       )]
       UpperCamelCase    :	int   	=		sorted(set(SCREAMING_SNAKE_CASE_       )       )
       UpperCamelCase    :	List[Any]   	=		len(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Any   	=		{b: str(SCREAMING_SNAKE_CASE_       ) for b, i in zip(SCREAMING_SNAKE_CASE_							,			range(SCREAMING_SNAKE_CASE_       )       )}

       UpperCamelCase    :	str   	=		[]
       rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''')       )
       rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''')       )
       rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''')       )
       rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''')       )
       rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''')       )

       for b in block_names:
              UpperCamelCase    :	Optional[int]   	=		block_name_mapping[b]
              rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")       )
              rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")       )
              rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")       )
              rename_keys.append(
                  (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")       )
              rename_keys.append(
                  (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")       )
              rename_keys.append(
                  (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")       )
              rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")       )
              rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")       )
              rename_keys.append(
                  (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")       )
              rename_keys.append(
                  (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")       )

              rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")       )
              rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")       )
              rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")       )
              rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")       )
              rename_keys.append(
                  (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")       )
              rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")       )
              rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")       )
              rename_keys.append(
                  (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")       )
              rename_keys.append(
                  (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")       )

       rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''')       )
       rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''')       )
       rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''')       )
       rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''')       )
       rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''')       )

       UpperCamelCase    :	Dict   	=		{}
       for item in rename_keys:
              if item[0] in original_param_names:
                     UpperCamelCase    :	str   	=		'''efficientnet.''' + item[1]

       UpperCamelCase    :	Tuple   	=		'''classifier.weight'''
       UpperCamelCase    :	List[Any]   	=		'''classifier.bias'''
       return key_mapping


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

              UpperCamelCase    :	int   	=		key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     UpperCamelCase    :	int   	=		torch.from_numpy(SCREAMING_SNAKE_CASE_       ).permute(3							,			2							,			0							,			1       )
              elif "depthwise_kernel" in key:
                     UpperCamelCase    :	Any   	=		torch.from_numpy(SCREAMING_SNAKE_CASE_       ).permute(2							,			3							,			0							,			1       )
              elif "kernel" in key:
                     UpperCamelCase    :	str   	=		torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE_       )       )
              else:
                     UpperCamelCase    :	Union[str, Any]   	=		torch.from_numpy(SCREAMING_SNAKE_CASE_       )

              # Replace HF parameters with original TF model parameters
              assert hf_params[hf_key].shape == new_hf_value.shape
              hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE_       )


@torch.no_grad()
def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	Any   	=		model_classes[model_name](
           include_top=SCREAMING_SNAKE_CASE_							,			weights='''imagenet'''							,			input_tensor=SCREAMING_SNAKE_CASE_							,			input_shape=SCREAMING_SNAKE_CASE_							,			pooling=SCREAMING_SNAKE_CASE_							,			classes=1_0_0_0							,			classifier_activation='''softmax'''							,			)

       UpperCamelCase    :	Union[str, Any]   	=		original_model.trainable_variables
       UpperCamelCase    :	int   	=		original_model.non_trainable_variables
       UpperCamelCase    :	List[str]   	=		{param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              UpperCamelCase    :	Tuple   	=		param.numpy()
       UpperCamelCase    :	Any   	=		list(tf_params.keys()       )

       # Load HuggingFace model
       UpperCamelCase    :	List[str]   	=		get_efficientnet_config(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		EfficientNetForImageClassification(SCREAMING_SNAKE_CASE_       ).eval()
       UpperCamelCase    :	List[str]   	=		hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print('''Converting parameters...'''       )
       UpperCamelCase    :	Optional[int]   	=		rename_keys(SCREAMING_SNAKE_CASE_       )
       replace_params(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

       # Initialize preprocessor and preprocess input image
       UpperCamelCase    :	List[str]   	=		convert_image_processor(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	str   	=		preprocessor(images=prepare_img()							,			return_tensors='''pt'''       )

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              UpperCamelCase    :	Optional[Any]   	=		hf_model(**SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[str]   	=		outputs.logits.detach().numpy()

       # Original model inference
       UpperCamelCase    :	str   	=		False
       UpperCamelCase    :	Tuple   	=		CONFIG_MAP[model_name]['''image_size''']
       UpperCamelCase    :	List[str]   	=		prepare_img().resize((image_size, image_size)							,			resample=PIL.Image.NEAREST       )
       UpperCamelCase    :	List[Any]   	=		image.img_to_array(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Any   	=		np.expand_dims(SCREAMING_SNAKE_CASE_							,			axis=0       )
       UpperCamelCase    :	Union[str, Any]   	=		original_model.predict(SCREAMING_SNAKE_CASE_       )

       # Check whether original and HF model outputs match  -> np.allclose
       assert np.allclose(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			atol=1E-3       ), "The predicted logits are not the same."
       print('''Model outputs match!'''       )

       if save_model:
              # Create folder to save model
              if not os.path.isdir(SCREAMING_SNAKE_CASE_       ):
                     os.mkdir(SCREAMING_SNAKE_CASE_       )
              # Save converted model and image processor
              hf_model.save_pretrained(SCREAMING_SNAKE_CASE_       )
              preprocessor.save_pretrained(SCREAMING_SNAKE_CASE_       )

       if push_to_hub:
              # Push model and image processor to hub
              print(F"""Pushing converted {model_name} to the hub..."""       )
              UpperCamelCase    :	List[str]   	=		F"""efficientnet-{model_name}"""
              preprocessor.push_to_hub(SCREAMING_SNAKE_CASE_       )
              hf_model.push_to_hub(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
  __UpperCAmelCase : str 			=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="b0",
      type=str,
      help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="hf_model",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument("--save_model", action="store_true", help="Save model to local")
  parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

  __UpperCAmelCase : List[Any] 			=				parser.parse_args()
  convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )



@require_tf
class    UpperCAmelCase_	:
       '''simple docstring'''
       __UpperCamelCase      :     Any							=					XGLMConfig
       __UpperCamelCase      :     Dict							=					{}
       __UpperCamelCase      :     List[str]							=					"gelu"






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=14 ,					__SCREAMING_SNAKE_CASE=7 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=99 ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=4 ,					__SCREAMING_SNAKE_CASE=37 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=512 ,					__SCREAMING_SNAKE_CASE=0.02 ,					):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		parent
              UpperCamelCase    :	Optional[int]   	=		batch_size
              UpperCamelCase    :	str   	=		seq_length
              UpperCamelCase    :	List[str]   	=		is_training
              UpperCamelCase    :	Tuple   	=		use_input_mask
              UpperCamelCase    :	Union[str, Any]   	=		use_labels
              UpperCamelCase    :	int   	=		vocab_size
              UpperCamelCase    :	Optional[int]   	=		d_model
              UpperCamelCase    :	Any   	=		num_hidden_layers
              UpperCamelCase    :	List[str]   	=		num_attention_heads
              UpperCamelCase    :	Optional[Any]   	=		ffn_dim
              UpperCamelCase    :	Optional[int]   	=		activation_function
              UpperCamelCase    :	List[str]   	=		activation_dropout
              UpperCamelCase    :	Any   	=		attention_dropout
              UpperCamelCase    :	str   	=		max_position_embeddings
              UpperCamelCase    :	Union[str, Any]   	=		initializer_range
              UpperCamelCase    :	int   	=		None
              UpperCamelCase    :	Dict   	=		0
              UpperCamelCase    :	int   	=		2
              UpperCamelCase    :	Any   	=		1






       def  _lowercase   (     self						):




              """simple docstring"""




              return XGLMConfig.from_pretrained('''facebook/xglm-564M'''						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		tf.clip_by_value(
                  ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size						) ,					clip_value_min=0 ,					clip_value_max=3						)

              UpperCamelCase    :	int   	=		None
              if self.use_input_mask:
                     UpperCamelCase    :	Any   	=		random_attention_mask([self.batch_size, self.seq_length]						)

              UpperCamelCase    :	Tuple   	=		self.get_config()

              UpperCamelCase    :	str   	=		floats_tensor([self.num_hidden_layers, self.num_attention_heads] ,					2						)

              return (
                  config,
                  input_ids,
                  input_mask,
                  head_mask,
              )






       def  _lowercase   (     self						):




              """simple docstring"""




              return XGLMConfig(
                  vocab_size=self.vocab_size ,					d_model=self.hidden_size ,					num_layers=self.num_hidden_layers ,					attention_heads=self.num_attention_heads ,					ffn_dim=self.ffn_dim ,					activation_function=self.activation_function ,					activation_dropout=self.activation_dropout ,					attention_dropout=self.attention_dropout ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					use_cache=__SCREAMING_SNAKE_CASE ,					bos_token_id=self.bos_token_id ,					eos_token_id=self.eos_token_id ,					pad_token_id=self.pad_token_id ,					return_dict=__SCREAMING_SNAKE_CASE ,					)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		self.prepare_config_and_inputs()

              (
                  (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, 
              )    :	Dict   	=		config_and_inputs

              UpperCamelCase    :	List[str]   	=		{
                  '''input_ids''': input_ids,
                  '''head_mask''': head_mask,
              }

              return config, inputs_dict




@require_tf
class    UpperCAmelCase_	(							_a,     _a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[Any]							=					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
       __UpperCamelCase      :     Union[str, Any]							=					(TFXGLMForCausalLM,) if is_tf_available() else ()
       __UpperCamelCase      :     Any							=					(
           {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
       )
       __UpperCamelCase      :     Optional[int]							=					False
       __UpperCamelCase      :     List[Any]							=					False
       __UpperCamelCase      :     List[Any]							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		TFXGLMModelTester(self						)
              UpperCamelCase    :	Any   	=		ConfigTester(self ,					config_class=__SCREAMING_SNAKE_CASE ,					n_embd=37						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.config_tester.run_common_tests()






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase    :	List[Any]   	=		TFXGLMModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






       @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              super().test_resize_token_embeddings()




@require_tf
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       @slow
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE=True						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)
              UpperCamelCase    :	List[Any]   	=		tf.convert_to_tensor([[2, 268, 9_865]] ,					dtype=tf.intaa						)  # The dog
              # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
              # fmt: off
              UpperCamelCase    :	str   	=		[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
              # fmt: on
              UpperCamelCase    :	Union[str, Any]   	=		model.generate(__SCREAMING_SNAKE_CASE ,					do_sample=__SCREAMING_SNAKE_CASE ,					num_beams=1						)
              if verify_outputs:
                     self.assertListEqual(output_ids[0].numpy().tolist() ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''						)
              UpperCamelCase    :	List[str]   	=		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)

              tf.random.set_seed(0						)
              UpperCamelCase    :	Tuple   	=		tokenizer('''Today is a nice day and''' ,					return_tensors='''tf'''						)
              UpperCamelCase    :	int   	=		tokenized.input_ids
              # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
              with tf.device(''':/CPU:0'''						):
                     UpperCamelCase    :	str   	=		model.generate(__SCREAMING_SNAKE_CASE ,					do_sample=__SCREAMING_SNAKE_CASE ,					seed=[7, 0]						)
              UpperCamelCase    :	Dict   	=		tokenizer.decode(output_ids[0] ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[int]   	=		(
                  '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
              )
              self.assertEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)
              UpperCamelCase    :	Tuple   	=		XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''						)

              UpperCamelCase    :	Tuple   	=		'''left'''

              # use different length sentences to test batching
              UpperCamelCase    :	Any   	=		[
                  '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
                  '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
                  '''regardless of whether left padding is applied or not. When''',
                  '''Hello, my dog is a little''',
              ]

              UpperCamelCase    :	List[Any]   	=		tokenizer(__SCREAMING_SNAKE_CASE ,					return_tensors='''tf''' ,					padding=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		inputs['''input_ids''']

              UpperCamelCase    :	Optional[int]   	=		model.generate(input_ids=__SCREAMING_SNAKE_CASE ,					attention_mask=inputs['''attention_mask'''] ,					max_new_tokens=12						)

              UpperCamelCase    :	Optional[int]   	=		tokenizer(sentences[0] ,					return_tensors='''tf'''						).input_ids
              UpperCamelCase    :	Optional[Any]   	=		model.generate(input_ids=__SCREAMING_SNAKE_CASE ,					max_new_tokens=12						)

              UpperCamelCase    :	str   	=		tokenizer(sentences[1] ,					return_tensors='''tf'''						).input_ids
              UpperCamelCase    :	List[Any]   	=		model.generate(input_ids=__SCREAMING_SNAKE_CASE ,					max_new_tokens=12						)

              UpperCamelCase    :	Any   	=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		tokenizer.decode(output_non_padded[0] ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Tuple   	=		tokenizer.decode(output_padded[0] ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[int]   	=		[
                  '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
                  '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
                  '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
                  '''a single''',
                  '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
              ]
              self.assertListEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              self.assertListEqual(__SCREAMING_SNAKE_CASE ,					[non_padded_sentence, padded_sentence]						)

