






import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

__UpperCamelCase      :      Tuple     						=						2


class        __UpperCamelCase    :







	def __init__(  self      :							List[str]       ,				*,  # begin keyword-only arguments
	    _lowerCAmelCase      :							Optional[int]="<s>"       ,				_lowerCAmelCase      :							Optional[int]="<pad>"       ,				_lowerCAmelCase      :							int="</s>"       ,				_lowerCAmelCase      :							str="<unk>"       ,				_lowerCAmelCase      :							List[str]=None       ,				)		-> Tuple:
		"""simple docstring"""







		__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  bos, unk, pad, eos
		__lowercase             =  []
		__lowercase             =  []
		__lowercase             =  {}
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		if extra_special_symbols:
			for s in extra_special_symbols:
				self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  len(self.symbols			)







	def __eq__(  self      :							Dict       ,				_lowerCAmelCase      :							List[str]			)		-> Any:
		"""simple docstring"""







		return self.indices == other.indices







	def __getitem__(  self      :							Any       ,				_lowerCAmelCase      :							str			)		-> Dict:
		"""simple docstring"""







		if idx < len(self.symbols			):
			return self.symbols[idx]
		return self.unk_word







	def __len__(  self      :							str			)		-> List[str]:
		"""simple docstring"""







		return len(self.symbols			)







	def __contains__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[Any]			)		-> Optional[int]:
		"""simple docstring"""







		return sym in self.indices







	@classmethod
	def  _a   (  cls      :							Dict       ,				_lowerCAmelCase      :							Optional[int]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  cls()
		d.add_from_file(_lowerCAmelCase			)
		return d







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Union[str, Any]=1       ,				_lowerCAmelCase      :							Optional[int]=False			)		-> Union[str, Any]:
		"""simple docstring"""







		if word in self.indices and not overwrite:
			__lowercase             =  self.indices[word]
			__lowercase             =  self.count[idx] + n
			return idx
		else:
			__lowercase             =  len(self.symbols			)
			__lowercase             =  idx
			self.symbols.append(_lowerCAmelCase			)
			self.count.append(_lowerCAmelCase			)
			return idx







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Optional[int]			)		-> Optional[Any]:
		"""simple docstring"""







		return 0







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Dict			)		-> str:
		"""simple docstring"""







		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			try:
				with open(_lowerCAmelCase       ,				"""r"""       ,				encoding="""utf-8"""			) as fd:
					self.add_from_file(_lowerCAmelCase			)
			except FileNotFoundError as fnfe:
				raise fnfe
			except UnicodeError:
				raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_lowerCAmelCase			)			)
			return

		__lowercase             =  f.readlines()
		__lowercase             =  self._load_meta(_lowerCAmelCase			)

		for line in lines[indices_start_line:]:
			try:
				__lowercase       ,						__lowercase             =  line.rstrip().rsplit(""" """       ,				1			)
				if field == "#fairseq:overwrite":
					__lowercase             =  True
					__lowercase       ,						__lowercase             =  line.rsplit(""" """       ,				1			)
				else:
					__lowercase             =  False
				__lowercase             =  int(_lowerCAmelCase			)
				__lowercase             =  line
				if word in self and not overwrite:
					raise RuntimeError(
					    """Duplicate word found when loading Dictionary: '{}'. """
					    """Duplicate words can overwrite earlier ones by adding the """
					    """#fairseq:overwrite flag at the end of the corresponding row """
					    """in the dictionary file. If using the Camembert model, please """
					    """download an updated copy of the model file.""".format(_lowerCAmelCase			)			)
				self.add_symbol(_lowerCAmelCase       ,				n=_lowerCAmelCase       ,				overwrite=_lowerCAmelCase			)
			except ValueError:
				raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""			)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  dict((re.sub(r"""@@$"""				,  """"""				,  lowerCamelCase					), v) if k.endswith("""@@"""					) else (re.sub(r"""$"""				,  """</w>"""				,  lowerCamelCase					), v) for k, v in d.items()					)
	__lowercase             =  """<s> <pad> </s> <unk>""".split()
	# restore the special tokens
	for k in keep_keys:
		del da[F'{k}</w>']
		__lowercase             =  d[k]  # restore
	return da





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if not os.path.exists(lowerCamelCase					):
		raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'					)
	os.makedirs(lowerCamelCase				,  exist_ok=lowerCamelCase					)
	print(F'Writing results to {pytorch_dump_folder_path}'					)

	# handle various types of models

	__lowercase             =  os.path.join(lowerCamelCase				,  """checkpoint.pt"""					)
	if not os.path.isfile(lowerCamelCase					):
		raise ValueError(F'path to the file {checkpoint_file} does not exist!'					)
	__lowercase             =  torch.load(lowerCamelCase				,  map_location="""cpu"""					)

	__lowercase             =  chkpt["""cfg"""]["""model"""]

	# dicts
	__lowercase             =  os.path.join(lowerCamelCase				,  """dict.txt"""					)
	if not os.path.isfile(lowerCamelCase					):
		raise ValueError(F'path to the file {dict_file} does not exist!'					)
	__lowercase             =  Dictionary.load(lowerCamelCase					)
	__lowercase             =  rewrite_dict_keys(src_dict.indices					)
	__lowercase             =  len(lowerCamelCase					)
	__lowercase             =  os.path.join(lowerCamelCase				,  VOCAB_FILES_NAMES["""vocab_file"""]					)
	print(F'Generating {src_vocab_file} of {src_vocab_size} records'					)
	with open(lowerCamelCase				,  """w"""				,  encoding="""utf-8"""					) as f:
		f.write(json.dumps(lowerCamelCase				,  ensure_ascii=lowerCamelCase				,  indent=lowerCamelCase					)					)

	# merges_file (bpecodes)
	__lowercase             =  os.path.join(lowerCamelCase				,  """bpecodes"""					)
	if not os.path.isfile(lowerCamelCase					):
		raise ValueError(F'path to the file {bpecodes_file} does not exist!'					)

	__lowercase             =  os.path.join(lowerCamelCase				,  VOCAB_FILES_NAMES["""merges_file"""]					)
	shutil.copyfile(lowerCamelCase				,  lowerCamelCase					)

	# model config
	__lowercase             =  os.path.join(lowerCamelCase				,  """config.json"""					)

	__lowercase             =  {
	    """activation_dropout""": args["""activation_dropout"""],
	    """architectures""": ["""BioGptForCausalLM"""],
	    """attention_probs_dropout_prob""": args["""attention_dropout"""],
	    """bos_token_id""": 0,
	    """eos_token_id""": 2,
	    """hidden_act""": args["""activation_fn"""],
	    """hidden_dropout_prob""": args["""dropout"""],
	    """hidden_size""": args["""decoder_embed_dim"""],
	    """initializer_range""": 0.02,
	    """intermediate_size""": args["""decoder_ffn_embed_dim"""],
	    """layer_norm_eps""": 1e-12,
	    """layerdrop""": args["""decoder_layerdrop"""],
	    """max_position_embeddings""": args["""max_target_positions"""],
	    """model_type""": """biogpt""",
	    """num_attention_heads""": args["""decoder_attention_heads"""],
	    """num_hidden_layers""": args["""decoder_layers"""],
	    """pad_token_id""": 1,
	    """scale_embedding""": not args["""no_scale_embedding"""],
	    """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
	    """vocab_size""": src_vocab_size,
	}

	# good hparam defaults to start with

	print(F'Generating {biogpt_model_config_file}'					)
	with open(lowerCamelCase				,  """w"""				,  encoding="""utf-8"""					) as f:
		f.write(json.dumps(lowerCamelCase				,  ensure_ascii=lowerCamelCase				,  indent=lowerCamelCase					)					)

	# tokenizer config
	__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)

	__lowercase             =  {
	    """bos_token""": """<s>""",
	    """eos_token""": """</s>""",
	    """model_max_length""": 1_024,
	    """pad_token""": """<pad>""",
	    """special_tokens_map_file""": None,
	    """tokenizer_class""": """BioGptTokenizer""",
	    """unk_token""": """<unk>""",
	}

	print(F'Generating {biogpt_tokenizer_config_file}'					)
	with open(lowerCamelCase				,  """w"""				,  encoding="""utf-8"""					) as f:
		f.write(json.dumps(lowerCamelCase				,  ensure_ascii=lowerCamelCase				,  indent=lowerCamelCase					)					)

	# model
	__lowercase             =  chkpt["""model"""]

	# remove unneeded keys
	__lowercase             =  [
	    """decoder.version""",
	]
	for k in ignore_keys:
		model_state_dict.pop(lowerCamelCase				,  lowerCamelCase					)

	__lowercase             =  list(model_state_dict.keys()					)
	for layer_name in layer_names:
		if layer_name.endswith("""output_projection.weight"""					):
			__lowercase             =  model_state_dict.pop(lowerCamelCase					)
		else:
			__lowercase             =  model_state_dict.pop(lowerCamelCase					)

	__lowercase             =  BioGptConfig.from_pretrained(lowerCamelCase					)
	__lowercase             =  BioGptForCausalLM(lowerCamelCase					)

	# check that it loads ok
	model_new.load_state_dict(lowerCamelCase					)

	# save
	__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)
	print(F'Generating {pytorch_weights_dump_path}'					)
	torch.save(lowerCamelCase				,  lowerCamelCase					)

	print("""Conversion is done!"""					)


if __name__ == "__main__":
						__UpperCamelCase      :      int     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--biogpt_checkpoint_path""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
						        """ bpecodes, etc."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						__UpperCamelCase      :      Optional[Any]     						=						parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









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 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if isinstance(lowerCamelCase				,  collections.abc.Iterable					):
		return x
	return (x, x)


@require_tf
class        __UpperCamelCase    :







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Dict=None       ,				**_lowerCAmelCase      :							int			)		-> str:
		"""simple docstring"""







		__lowercase             =  VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase       ,				_lowerCAmelCase			)

		__lowercase             =  TFVisionTextDualEncoderModel(_lowerCAmelCase			)

		__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)

		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  _a   (  self      :							str       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							int=None       ,				**_lowerCAmelCase      :							Dict			)		-> Tuple:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase       ,				text_model=_lowerCAmelCase			)

		__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)

		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  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Any=None       ,				**_lowerCAmelCase      :							str			)		-> Tuple:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  {"""vision_model""": vision_model, """text_model""": text_model}
		__lowercase             =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase			)

		__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)

		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  _a   (  self      :							Dict       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							List[Any]=None       ,				**_lowerCAmelCase      :							Any			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase       ,				text_model=_lowerCAmelCase			)

		__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)
		__lowercase             =  output[0].numpy()

		with tempfile.TemporaryDirectory() as tmpdirname:
			model.save_pretrained(_lowerCAmelCase			)
			__lowercase             =  TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase			)

			__lowercase             =  model(input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase			)
			__lowercase             =  after_output[0].numpy()
			__lowercase             =  np.amax(np.abs(out_a - out_a			)			)
			self.assertLessEqual(_lowerCAmelCase       ,				1e-5			)







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[int]=None       ,				**_lowerCAmelCase      :							Optional[int]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase       ,				text_model=_lowerCAmelCase			)

		__lowercase             =  model(
		    input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase       ,				output_attentions=_lowerCAmelCase			)

		__lowercase             =  output.vision_model_output.attentions
		self.assertEqual(len(_lowerCAmelCase			)       ,				vision_config.num_hidden_layers			)

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

		__lowercase             =  output.text_model_output.attentions
		self.assertEqual(len(_lowerCAmelCase			)       ,				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  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							np.ndarray       ,				_lowerCAmelCase      :							np.ndarray       ,				_lowerCAmelCase      :							float			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  np.abs((a - b)			).max()
		self.assertLessEqual(_lowerCAmelCase       ,				_lowerCAmelCase       ,				F'Difference between torch and flax is {diff} (>= {tol}).'			)







	def  _a   (  self      :							List[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_vision_text_dual_encoder_model(**_lowerCAmelCase			)







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_model_from_pretrained_configs(**_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> int:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase			)







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_save_load(**_lowerCAmelCase			)







	def  _a   (  self      :							Dict			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  self.prepare_config_and_inputs()
		self.check_vision_text_output_attention(**_lowerCAmelCase			)







	@slow
	def  _a   (  self      :							Optional[int]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_pretrained_model_and_inputs()

		__lowercase             =  model_a(**_lowerCAmelCase			)
		__lowercase             =  outputs[0].numpy()

		with tempfile.TemporaryDirectory() as tmp_dirname:
			model_a.save_pretrained(_lowerCAmelCase			)
			__lowercase             =  TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase			)

			__lowercase             =  model_a(**_lowerCAmelCase			)
			__lowercase             =  after_outputs[0].numpy()
			__lowercase             =  np.amax(np.abs(out_a - out_a			)			)
			self.assertLessEqual(_lowerCAmelCase       ,				1e-5			)


@require_tf
class        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):







	def  _a   (  self      :							Optional[int]			)		-> List[str]:
		"""simple docstring"""







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

		return model, inputs







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							List[str]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  TFViTModel(_lowerCAmelCase       ,				name="""vision_model"""			)
		__lowercase             =  TFBertModel(_lowerCAmelCase       ,				name="""text_model"""			)
		return vision_model, text_model







	def  _a   (  self      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  TFViTModelTester(self			)
		__lowercase             =  TFBertModelTester(self			)
		__lowercase             =  vit_model_tester.prepare_config_and_inputs()
		__lowercase             =  bert_model_tester.prepare_config_and_inputs()

		__lowercase       ,						__lowercase       ,						__lowercase             =  vision_config_and_inputs

		(
		    (
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						
		)             =  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        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):







	def  _a   (  self      :							Tuple			)		-> Any:
		"""simple docstring"""







		__lowercase             =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
		    """Rocketknight1/tiny-random-deit-tf"""       ,				"""hf-internal-testing/tiny-random-roberta"""			)
		__lowercase             =  13
		__lowercase             =  floats_tensor(
		    [
		        batch_size,
		        model.vision_model.config.num_channels,
		        model.vision_model.config.image_size,
		        model.vision_model.config.image_size,
		    ]			)
		__lowercase             =  ids_tensor([batch_size, 4]       ,				model.text_model.config.vocab_size			)
		__lowercase             =  random_attention_mask([batch_size, 4]			)
		__lowercase             =  {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

		return model, inputs







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							int=None       ,				**_lowerCAmelCase      :							Tuple			)		-> Dict:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.get_vision_text_model(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase       ,				text_model=_lowerCAmelCase			)

		__lowercase             =  model(
		    input_ids=_lowerCAmelCase       ,				pixel_values=_lowerCAmelCase       ,				attention_mask=_lowerCAmelCase       ,				output_attentions=_lowerCAmelCase			)

		__lowercase             =  output.vision_model_output.attentions
		self.assertEqual(len(_lowerCAmelCase			)       ,				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)
		__lowercase             =  to_atuple(vision_model.config.image_size			)
		__lowercase             =  to_atuple(vision_model.config.patch_size			)
		__lowercase             =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
		__lowercase             =  num_patches + 2
		self.assertEqual(vision_attentions[0].shape[-3:]       ,				(vision_config.num_attention_heads, seq_len, seq_len)			)

		__lowercase             =  output.text_model_output.attentions
		self.assertEqual(len(_lowerCAmelCase			)       ,				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  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Dict			)		-> int:
		"""simple docstring"""







		__lowercase             =  TFDeiTModel(_lowerCAmelCase       ,				name="""vision_model"""			)
		__lowercase             =  TFRobertaModel(_lowerCAmelCase       ,				name="""text_model"""			)
		return vision_model, text_model







	def  _a   (  self      :							Tuple			)		-> str:
		"""simple docstring"""







		__lowercase             =  TFDeiTModelTester(self			)
		__lowercase             =  TFRobertaModelTester(self			)
		__lowercase             =  vit_model_tester.prepare_config_and_inputs()
		__lowercase             =  bert_model_tester.prepare_config_and_inputs()

		__lowercase       ,						__lowercase       ,						__lowercase             =  vision_config_and_inputs

		(
		    (
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						
		)             =  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        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):







	def  _a   (  self      :							int			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
		    """Rocketknight1/tiny-random-clip-tf"""       ,				"""hf-internal-testing/tiny-random-bert"""			)
		__lowercase             =  13
		__lowercase             =  floats_tensor(
		    [
		        batch_size,
		        model.vision_model.config.num_channels,
		        model.vision_model.config.image_size,
		        model.vision_model.config.image_size,
		    ]			)
		__lowercase             =  ids_tensor([batch_size, 4]       ,				model.text_model.config.vocab_size			)
		__lowercase             =  random_attention_mask([batch_size, 4]			)
		__lowercase             =  {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

		return model, inputs







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  TFCLIPVisionModel(_lowerCAmelCase       ,				name="""vision_model"""			)
		__lowercase             =  TFBertModel(_lowerCAmelCase       ,				name="""text_model"""			)
		return vision_model, text_model







	def  _a   (  self      :							Union[str, Any]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  TFCLIPVisionModelTester(self			)
		__lowercase             =  TFBertModelTester(self			)
		__lowercase             =  clip_model_tester.prepare_config_and_inputs()
		__lowercase             =  bert_model_tester.prepare_config_and_inputs()

		__lowercase       ,						__lowercase             =  vision_config_and_inputs

		(
		    (
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						(
		    __lowercase
		)       ,						
		)             =  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        __UpperCamelCase    (     unittest.TestCase   ):







	@slow
	def  _a   (  self      :							int			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  TFVisionTextDualEncoderModel.from_pretrained(
		    """clip-italian/clip-italian"""       ,				logit_scale_init_value=1.0       ,				from_pt=_lowerCAmelCase			)
		__lowercase             =  VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""			)

		__lowercase             =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
		__lowercase             =  processor(
		    text=["""una foto di un gatto""", """una foto di un cane"""]       ,				images=_lowerCAmelCase       ,				padding=_lowerCAmelCase       ,				return_tensors="""np"""			)

		__lowercase             =  model(**_lowerCAmelCase			)

		# 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])       ,				)

		__lowercase             =  np.array([[1.2_284_727, 0.3_104_122]]			)

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

