





import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case  :    str						    =  logging.get_logger(__name__)

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			List[Any]	):
   __lowerCAmelCase								=	[]
   for i in range(encoder_config.num_hidden_layers	):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (F"""encoder.deit.blocks.{i}.norm1.weight""", F"""encoder.encoder.layer.{i}.layernorm_before.weight""")	)
      rename_keys.append((F"""encoder.deit.blocks.{i}.norm1.bias""", F"""encoder.encoder.layer.{i}.layernorm_before.bias""")	)
      rename_keys.append(
          (F"""encoder.deit.blocks.{i}.attn.proj.weight""", F"""encoder.encoder.layer.{i}.attention.output.dense.weight""")	)
      rename_keys.append(
          (F"""encoder.deit.blocks.{i}.attn.proj.bias""", F"""encoder.encoder.layer.{i}.attention.output.dense.bias""")	)
      rename_keys.append(
          (F"""encoder.deit.blocks.{i}.norm2.weight""", F"""encoder.encoder.layer.{i}.layernorm_after.weight""")	)
      rename_keys.append((F"""encoder.deit.blocks.{i}.norm2.bias""", F"""encoder.encoder.layer.{i}.layernorm_after.bias""")	)
      rename_keys.append(
          (F"""encoder.deit.blocks.{i}.mlp.fc1.weight""", F"""encoder.encoder.layer.{i}.intermediate.dense.weight""")	)
      rename_keys.append(
          (F"""encoder.deit.blocks.{i}.mlp.fc1.bias""", F"""encoder.encoder.layer.{i}.intermediate.dense.bias""")	)
      rename_keys.append(
          (F"""encoder.deit.blocks.{i}.mlp.fc2.weight""", F"""encoder.encoder.layer.{i}.output.dense.weight""")	)
      rename_keys.append((F"""encoder.deit.blocks.{i}.mlp.fc2.bias""", F"""encoder.encoder.layer.{i}.output.dense.bias""")	)

   # cls token, position embeddings and patch embeddings of encoder
   rename_keys.extend(
       [
           ('encoder.deit.cls_token', 'encoder.embeddings.cls_token'),
           ('encoder.deit.pos_embed', 'encoder.embeddings.position_embeddings'),
           ('encoder.deit.patch_embed.proj.weight', 'encoder.embeddings.patch_embeddings.projection.weight'),
           ('encoder.deit.patch_embed.proj.bias', 'encoder.embeddings.patch_embeddings.projection.bias'),
           ('encoder.deit.norm.weight', 'encoder.layernorm.weight'),
           ('encoder.deit.norm.bias', 'encoder.layernorm.bias'),
       ]	)

   return rename_keys

def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Optional[int]	):
   for i in range(encoder_config.num_hidden_layers	):
      # queries, keys and values (only weights, no biases)
      __lowerCAmelCase								=	state_dict.pop(F"""encoder.deit.blocks.{i}.attn.qkv.weight"""	)

      __lowerCAmelCase								=	in_proj_weight[
          : encoder_config.hidden_size, :
      ]
      __lowerCAmelCase								=	in_proj_weight[
          encoder_config.hidden_size : encoder_config.hidden_size * 2, :
      ]
      __lowerCAmelCase								=	in_proj_weight[
          -encoder_config.hidden_size :, :
      ]

def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	dct.pop(lowerCAmelCase_	)
   __lowerCAmelCase								=	val

def   a_    ( lowerCAmelCase_      :			Union[str, Any]	):
   if "handwritten" in checkpoint_url:
      __lowerCAmelCase								=	'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'  # industry
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
      # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
   elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCAmelCase								=	'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
   __lowerCAmelCase								=	Image.open(requests.get(lowerCAmelCase_,	stream=lowerCAmelCase_	).raw	).convert('RGB'	)
   return im

@torch.no_grad()
def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			int	):
   __lowerCAmelCase								=	ViTConfig(image_size=384,	qkv_bias=lowerCAmelCase_	)
   __lowerCAmelCase								=	TrOCRConfig()

   # size of the architecture
   if "base" in checkpoint_url:
      __lowerCAmelCase								=	768
   elif "large" in checkpoint_url:
      # use ViT-large encoder
      __lowerCAmelCase								=	1024
      __lowerCAmelCase								=	4096
      __lowerCAmelCase								=	24
      __lowerCAmelCase								=	16
      __lowerCAmelCase								=	1024
   else:
      raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'	)

   # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
   if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
      __lowerCAmelCase								=	False
      __lowerCAmelCase								=	'relu'
      __lowerCAmelCase								=	1024
      __lowerCAmelCase								=	True
      __lowerCAmelCase								=	False
      __lowerCAmelCase								=	False

   # load HuggingFace model
   __lowerCAmelCase								=	ViTModel(lowerCAmelCase_,	add_pooling_layer=lowerCAmelCase_	)
   __lowerCAmelCase								=	TrOCRForCausalLM(lowerCAmelCase_	)
   __lowerCAmelCase								=	VisionEncoderDecoderModel(encoder=lowerCAmelCase_,	decoder=lowerCAmelCase_	)
   model.eval()

   # load state_dict of original model, rename some keys
   __lowerCAmelCase								=	torch.hub.load_state_dict_from_url(lowerCAmelCase_,	map_location='cpu',	check_hash=lowerCAmelCase_	)['model']

   __lowerCAmelCase								=	create_rename_keys(lowerCAmelCase_,	lowerCAmelCase_	)
   for src, dest in rename_keys:
      rename_key(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)
   read_in_q_k_v(lowerCAmelCase_,	lowerCAmelCase_	)

   # remove parameters we don't need
   del state_dict["encoder.deit.head.weight"]
   del state_dict["encoder.deit.head.bias"]
   del state_dict["decoder.version"]

   # add prefix to decoder keys
   for key, val in state_dict.copy().items():
      __lowerCAmelCase								=	state_dict.pop(lowerCAmelCase_	)
      if key.startswith('decoder'	) and "output_projection" not in key:
         __lowerCAmelCase								=	val
      else:
         __lowerCAmelCase								=	val

    # load state dict
   model.load_state_dict(lowerCAmelCase_	)

   # Check outputs on an image
   __lowerCAmelCase								=	ViTImageProcessor(size=encoder_config.image_size	)
   __lowerCAmelCase								=	RobertaTokenizer.from_pretrained('roberta-large'	)
   __lowerCAmelCase								=	TrOCRProcessor(lowerCAmelCase_,	lowerCAmelCase_	)

   __lowerCAmelCase								=	processor(images=prepare_img(lowerCAmelCase_	),	return_tensors='pt'	).pixel_values

   # verify logits
   __lowerCAmelCase								=	torch.tensor([[model.config.decoder.decoder_start_token_id]]	)
   __lowerCAmelCase								=	model(pixel_values=lowerCAmelCase_,	decoder_input_ids=lowerCAmelCase_	)
   __lowerCAmelCase								=	outputs.logits

   __lowerCAmelCase								=	torch.Size([1, 1, 5_0265]	)
   if "trocr-base-handwritten" in checkpoint_url:
      __lowerCAmelCase								=	torch.tensor(
          [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]	)
   elif "trocr-large-handwritten" in checkpoint_url:
      __lowerCAmelCase								=	torch.tensor(
          [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]	)
   elif "trocr-base-printed" in checkpoint_url:
      __lowerCAmelCase								=	torch.tensor(
          [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]	)
   elif "trocr-large-printed" in checkpoint_url:
      __lowerCAmelCase								=	torch.tensor(
          [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]	)

   if "stage1" not in checkpoint_url:
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      assert torch.allclose(logits[0, 0, :10],	lowerCAmelCase_,	atol=1E-3	), "First elements of logits not as expected"

   Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
   print(F"""Saving model to {pytorch_dump_folder_path}"""	)
   model.save_pretrained(lowerCAmelCase_	)
   print(F"""Saving processor to {pytorch_dump_folder_path}"""	)
   processor.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
     _snake_case  :    Optional[Any]						    =  argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_url',
         default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
         type=str,
         help='URL to the original PyTorch checkpoint (.pth file).',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
     )
     _snake_case  :    Tuple						    =  parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel

def   a_    ( lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Optional[int]=None,	lowerCAmelCase_      :			List[Any]=None	):
   if attention_mask is None:
      __lowerCAmelCase								=	tf.cast(tf.math.not_equal(lowerCAmelCase_,	config.pad_token_id	),	tf.inta	)
   return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class 				_UpperCAmelCase    :

  """simple docstring"""


  a_        = OPTConfig
  a_        = {}
  a_        = """gelu"""






  def __init__(				self :		Dict					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		List[str]=1_3					,  lowerCAmelCase_ :		Tuple=7					,  lowerCAmelCase_ :		Dict=True					,  lowerCAmelCase_ :		Union[str, Any]=False					,  lowerCAmelCase_ :		Any=9_9					,  lowerCAmelCase_ :		Any=1_6					,  lowerCAmelCase_ :		List[str]=2					,  lowerCAmelCase_ :		Dict=4					,  lowerCAmelCase_ :		str=4					,  lowerCAmelCase_ :		Any="gelu"					,  lowerCAmelCase_ :		Optional[Any]=0.1					,  lowerCAmelCase_ :		List[Any]=0.1					,  lowerCAmelCase_ :		Tuple=2_0					,  lowerCAmelCase_ :		Optional[Any]=2					,  lowerCAmelCase_ :		Any=1					,  lowerCAmelCase_ :		List[Any]=0					,  lowerCAmelCase_ :		Optional[int]=1_6					,  lowerCAmelCase_ :		Dict=1_6					,  )							->						int:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	seq_length
     __lowerCAmelCase								=	is_training
     __lowerCAmelCase								=	use_labels
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	intermediate_size
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	attention_probs_dropout_prob
     __lowerCAmelCase								=	max_position_embeddings
     __lowerCAmelCase								=	eos_token_id
     __lowerCAmelCase								=	pad_token_id
     __lowerCAmelCase								=	bos_token_id
     __lowerCAmelCase								=	embed_dim
     __lowerCAmelCase								=	word_embed_proj_dim
     __lowerCAmelCase								=	False






  def 				lowercase		(				self :		List[str]     )							->						Optional[Any]:
     __lowerCAmelCase								=	ids_tensor([self.batch_size, self.seq_length - 1]					,  self.vocab_size     )
     __lowerCAmelCase								=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )					,  1     )
     __lowerCAmelCase								=	tf.concat([input_ids, eos_tensor]					,  axis=1     )

     __lowerCAmelCase								=	self.config_cls(
         vocab_size=self.vocab_size					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  ffn_dim=self.intermediate_size					,  dropout=self.hidden_dropout_prob					,  attention_dropout=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.bos_token_id					,  pad_token_id=self.pad_token_id					,  embed_dim=self.embed_dim					,  word_embed_proj_dim=self.word_embed_proj_dim					,  is_encoder_decoder=lowerCAmelCase_					,  **self.config_updates					,  )
     __lowerCAmelCase								=	prepare_opt_inputs_dict(lowerCAmelCase_					,  lowerCAmelCase_     )
     return config, inputs_dict






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		Optional[Any]					,  lowerCAmelCase_ :		Dict     )							->						List[str]:
     __lowerCAmelCase								=	TFOPTModel(config=lowerCAmelCase_     )
     __lowerCAmelCase								=	inputs_dict['input_ids']

     __lowerCAmelCase								=	input_ids[:1, :]
     __lowerCAmelCase								=	inputs_dict['attention_mask'][:1, :]
     __lowerCAmelCase								=	1

     # first forward pass
     __lowerCAmelCase								=	model(lowerCAmelCase_					,  attention_mask=lowerCAmelCase_					,  use_cache=lowerCAmelCase_     )

     __lowerCAmelCase	,	__lowerCAmelCase								=	outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     __lowerCAmelCase								=	ids_tensor((self.batch_size, 3)					,  config.vocab_size     )
     __lowerCAmelCase								=	tf.cast(ids_tensor((self.batch_size, 3)					,  2     )					,  tf.inta     )

     # append to next input_ids and
     __lowerCAmelCase								=	tf.concat([input_ids, next_tokens]					,  axis=-1     )
     __lowerCAmelCase								=	tf.concat([attention_mask, next_attn_mask]					,  axis=-1     )

     __lowerCAmelCase								=	model(lowerCAmelCase_					,  attention_mask=lowerCAmelCase_     )[0]
     __lowerCAmelCase								=	model(lowerCAmelCase_					,  attention_mask=lowerCAmelCase_					,  past_key_values=lowerCAmelCase_     )[0]

     self.parent.assertEqual(next_tokens.shape[1]					,  output_from_past.shape[1]     )

     # select random slice
     __lowerCAmelCase								=	int(ids_tensor((1,)					,  output_from_past.shape[-1]     )     )
     __lowerCAmelCase								=	output_from_no_past[:, -3:, random_slice_idx]
     __lowerCAmelCase								=	output_from_past[:, :, random_slice_idx]

     # test that outputs are equal for slice
     tf.debugging.assert_near(lowerCAmelCase_					,  lowerCAmelCase_					,  rtol=1e-3     )





@require_tf
class 				_UpperCAmelCase    (    _UpperCamelCase		,    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
  a_        = (TFOPTForCausalLM,) if is_tf_available() else ()
  a_        = (
      {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
  )
  a_        = False
  a_        = False
  a_        = False
  a_        = 10






  def 				lowercase		(				self :		List[str]     )							->						Optional[int]:
     __lowerCAmelCase								=	TFOPTModelTester(self     )
     __lowerCAmelCase								=	ConfigTester(self					,  config_class=lowerCAmelCase_     )






  def 				lowercase		(				self :		Tuple     )							->						Tuple:
     self.config_tester.run_common_tests()






  def 				lowercase		(				self :		Tuple     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						Dict:
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.model_tester.prepare_config_and_inputs_for_common()

     def _get_word_embedding_weight(lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Union[str, Any]     ):
        if hasattr(lowerCAmelCase_					,  'weight'     ):
           return embedding_layer.weight
        else:
           # Here we build the word embeddings weights if not exists.
           # And then we retry to get the attribute once built.
           model.build()
           if hasattr(lowerCAmelCase_					,  'weight'     ):
              return embedding_layer.weight
           else:
              return None

     for model_class in self.all_model_classes:
        for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
           # build the embeddings
           __lowerCAmelCase								=	model_class(config=lowerCAmelCase_     )
           __lowerCAmelCase								=	_get_word_embedding_weight(lowerCAmelCase_					,  model.get_input_embeddings()     )
           __lowerCAmelCase								=	_get_word_embedding_weight(lowerCAmelCase_					,  model.get_output_embeddings()     )

           # reshape the embeddings
           model.resize_token_embeddings(lowerCAmelCase_     )
           __lowerCAmelCase								=	_get_word_embedding_weight(lowerCAmelCase_					,  model.get_input_embeddings()     )
           __lowerCAmelCase								=	_get_word_embedding_weight(lowerCAmelCase_					,  model.get_output_embeddings()     )

           # check that the resized embeddings size matches the desired size.
           __lowerCAmelCase								=	size if size is not None else config.vocab_size

           self.assertEqual(new_input_embeddings.shape[0]					,  lowerCAmelCase_     )

           # check that weights remain the same after resizing
           __lowerCAmelCase								=	True
           for pa, pa in zip(old_input_embeddings.value()					,  new_input_embeddings.value()     ):
              if tf.math.reduce_sum(tf.math.abs(pa - pa     )     ) > 0:
                 __lowerCAmelCase								=	False
           self.assertTrue(lowerCAmelCase_     )

           if old_output_embeddings is not None and new_output_embeddings is not None:
              self.assertEqual(new_output_embeddings.shape[0]					,  lowerCAmelCase_     )

              __lowerCAmelCase								=	True
              for pa, pa in zip(old_output_embeddings.value()					,  new_output_embeddings.value()     ):
                 if tf.math.reduce_sum(tf.math.abs(pa - pa     )     ) > 0:
                    __lowerCAmelCase								=	False
              self.assertTrue(lowerCAmelCase_     )





def   a_    ( lowerCAmelCase_      :			Union[str, Any]	):
   return tf.constant(lowerCAmelCase_,	dtype=tf.intaa	)



@require_tf
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  a_        = 99






  def 				lowercase		(				self :		Optional[int]     )							->						Any:
     __lowerCAmelCase								=	tf.ones((4, 1)					,  dtype=tf.intaa     ) * 2
     __lowerCAmelCase								=	tf.concat([ids_tensor((4, 6)					,  self.vocab_size - 3     ) + 3, eos_column_vector]					,  axis=1     )
     __lowerCAmelCase								=	input_ids.shape[0]
     __lowerCAmelCase								=	OPTConfig(
         vocab_size=self.vocab_size					,  hidden_size=2_4					,  num_hidden_layers=2					,  num_attention_heads=2					,  ffn_dim=3_2					,  max_position_embeddings=4_8					,  eos_token_id=2					,  pad_token_id=1					,  bos_token_id=0					,  )
     return config, input_ids, batch_size





@require_sentencepiece
@require_tf
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  @slow
  def 				lowercase		(				self :		str     )							->						List[str]:
     __lowerCAmelCase								=	TFOPTModel.from_pretrained('facebook/opt-350m'     )
     __lowerCAmelCase								=	_long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]     )
     __lowerCAmelCase								=	tf.not_equal(lowerCAmelCase_					,  model.config.pad_token_id     )
     with tf.GradientTape():
        __lowerCAmelCase								=	model(input_ids=lowerCAmelCase_					,  attention_mask=lowerCAmelCase_     ).last_hidden_state
     __lowerCAmelCase								=	(1, 1_1, 5_1_2)
     self.assertEqual(output.shape					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	tf.constant(
         [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]]     )
     self.assertTrue(np.allclose(output[:, :3, :3]					,  lowerCAmelCase_					,  atol=4e-3     )     )

     __lowerCAmelCase								=	tf.function(lowerCAmelCase_					,  jit_compile=lowerCAmelCase_     )
     __lowerCAmelCase								=	xla_generate(lowerCAmelCase_					,  lowerCAmelCase_     )[0]
     self.assertTrue(np.allclose(output[:, :3, :3]					,  lowerCAmelCase_					,  atol=4e-2     )     )





@require_tf
@slow
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		int     )							->						Dict:
     super().setUp()
     __lowerCAmelCase								=	'facebook/opt-350m'






  def 				lowercase		(				self :		Dict     )							->						Any:
     __lowerCAmelCase								=	TFOPTForCausalLM.from_pretrained(self.path_model     )
     __lowerCAmelCase								=	GPTaTokenizer.from_pretrained(self.path_model     )

     __lowerCAmelCase								=	[
         'Today is a beautiful day and I want to',
         'In the city of',
         'Paris is the capital of France and',
         'Computers and mobile phones have taken',
     ]
     # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
     __lowerCAmelCase								=	tokenizer(lowerCAmelCase_					,  return_tensors='tf'					,  padding=lowerCAmelCase_					,  add_special_tokens=lowerCAmelCase_     )
     __lowerCAmelCase								=	tf.math.reduce_mean(model(inputs.input_ids					,  attention_mask=inputs.attention_mask     )[0]					,  axis=-1     )
     __lowerCAmelCase								=	tf.constant(
         [
             [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
             [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
             [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
             [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
         ]     )
     self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-4     )     )

     __lowerCAmelCase								=	tf.function(lowerCAmelCase_					,  jit_compile=lowerCAmelCase_     )
     __lowerCAmelCase								=	tf.math.reduce_mean(xla_generate(inputs.input_ids					,  attention_mask=inputs.attention_mask     )[0]					,  axis=-1     )
     self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-4     )     )





@require_tf
@slow
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  @property
  def 				lowercase		(				self :		Optional[int]     )							->						int:
     return [
         "Today is a beautiful day and I want",
         "In the city of",
         "Paris is the capital of France and",
         "Computers and mobile phones have taken",
     ]






  def 				lowercase		(				self :		int     )							->						str:
     __lowerCAmelCase								=	'facebook/opt-125m'

     __lowerCAmelCase								=	[
         'Today is a beautiful day and I want to',
         'In the city of New York, the city',
         'Paris is the capital of France and the capital',
         'Computers and mobile phones have taken over the',
     ]

     __lowerCAmelCase								=	[]
     __lowerCAmelCase								=	GPTaTokenizer.from_pretrained(lowerCAmelCase_     )
     __lowerCAmelCase								=	TFOPTForCausalLM.from_pretrained(lowerCAmelCase_     )

     for prompt in self.prompts:
        __lowerCAmelCase								=	tokenizer(lowerCAmelCase_					,  return_tensors='tf'     ).input_ids

        __lowerCAmelCase								=	model.generate(lowerCAmelCase_					,  max_length=1_0     )

        __lowerCAmelCase								=	tokenizer.batch_decode(lowerCAmelCase_					,  skip_special_tokens=lowerCAmelCase_     )
        predicted_outputs += generated_string

     self.assertListEqual(lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]     )							->						str:
     __lowerCAmelCase								=	'facebook/opt-350m'

     __lowerCAmelCase								=	GPTaTokenizer.from_pretrained(lowerCAmelCase_     )
     __lowerCAmelCase								=	TFOPTForCausalLM.from_pretrained(lowerCAmelCase_     )

     __lowerCAmelCase								=	'left'

     # use different length sentences to test batching
     __lowerCAmelCase								=	[
         'Hello, my dog is a little',
         'Today, I',
     ]

     __lowerCAmelCase								=	tokenizer(lowerCAmelCase_					,  return_tensors='tf'					,  padding=lowerCAmelCase_     )
     __lowerCAmelCase								=	inputs['input_ids']

     __lowerCAmelCase								=	model.generate(input_ids=lowerCAmelCase_					,  attention_mask=inputs['attention_mask']     )

     __lowerCAmelCase								=	tokenizer(sentences[0]					,  return_tensors='tf'     ).input_ids
     __lowerCAmelCase								=	model.generate(input_ids=lowerCAmelCase_     )

     __lowerCAmelCase								=	inputs_non_padded.shape[-1] - tf.math.reduce_sum(
         tf.cast(inputs['attention_mask'][-1]					,  tf.intaa     )     )
     __lowerCAmelCase								=	tokenizer(sentences[1]					,  return_tensors='tf'     ).input_ids
     __lowerCAmelCase								=	model.generate(input_ids=lowerCAmelCase_					,  max_length=model.config.max_length - num_paddings     )

     __lowerCAmelCase								=	tokenizer.batch_decode(lowerCAmelCase_					,  skip_special_tokens=lowerCAmelCase_     )
     __lowerCAmelCase								=	tokenizer.decode(output_non_padded[0]					,  skip_special_tokens=lowerCAmelCase_     )
     __lowerCAmelCase								=	tokenizer.decode(output_padded[0]					,  skip_special_tokens=lowerCAmelCase_     )

     __lowerCAmelCase								=	[
         'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
         'Today, I was in the middle of a conversation with a friend about the',
     ]
     self.assertListEqual(lowerCAmelCase_					,  lowerCAmelCase_     )
     self.assertListEqual(lowerCAmelCase_					,  [non_padded_sentence, padded_sentence]     )






  def 				lowercase		(				self :		List[Any]     )							->						List[Any]:
     __lowerCAmelCase								=	'facebook/opt-350m'

     __lowerCAmelCase								=	[
         'Today is a beautiful day and I want to',
         'In the city of San Francisco, the city',
         'Paris is the capital of France and the capital',
         'Computers and mobile phones have taken over the',
     ]

     __lowerCAmelCase								=	[]
     __lowerCAmelCase								=	GPTaTokenizer.from_pretrained(lowerCAmelCase_     )
     __lowerCAmelCase								=	TFOPTForCausalLM.from_pretrained(lowerCAmelCase_     )

     for prompt in self.prompts:
        __lowerCAmelCase								=	tokenizer(lowerCAmelCase_					,  return_tensors='tf'     ).input_ids

        __lowerCAmelCase								=	model.generate(lowerCAmelCase_					,  max_length=1_0     )

        __lowerCAmelCase								=	tokenizer.batch_decode(lowerCAmelCase_					,  skip_special_tokens=lowerCAmelCase_     )
        predicted_outputs += generated_string

     self.assertListEqual(lowerCAmelCase_					,  lowerCAmelCase_     )

