






import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__UpperCamelCase      :      Union[str, Any]     						=						logging.get_logger(__name__)

__UpperCamelCase      :      Dict     						=						{"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

__UpperCamelCase      :      str     						=						{
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

__UpperCamelCase      :      Dict     						=						{
    """abeja/gpt-neox-japanese-2.7b""": 2048,
}





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	with open(lowerCamelCase				,  """r"""				,  encoding="""utf-8"""					) as f:
		__lowercase             =  json.loads(f.read()					)

	__lowercase             =  collections.OrderedDict()
	__lowercase             =  collections.OrderedDict()
	__lowercase             =  collections.OrderedDict()
	with open(lowerCamelCase				,  """r"""				,  encoding="""utf-8"""					) as f:
		__lowercase             =  f.readlines()
	__lowercase             =  [[t.rstrip("""\n"""					)] if (t == """,""" or """,""" not in t) else t.rstrip("""\n"""					).split(""","""					) for t in token]
	for idx, b in enumerate(lowerCamelCase					):
		__lowercase             =  b
		__lowercase             =  idx
		for wd in b:
			__lowercase             =  idx

	return vocab, raw_vocab, ids_to_tokens, emoji


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Optional[int]   =       VOCAB_FILES_NAMES
	__snake_case	:Union[str, Any]   =       PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:List[Any]   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:Union[str, Any]   =       ['input_ids', 'attention_mask']







	def __init__(  self      :							Tuple       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							Dict="<|endoftext|>"       ,				_lowerCAmelCase      :							Union[str, Any]="<|endoftext|>"       ,				_lowerCAmelCase      :							Union[str, Any]="<|startoftext|>"       ,				_lowerCAmelCase      :							Any="<|endoftext|>"       ,				_lowerCAmelCase      :							Union[str, Any]=False       ,				**_lowerCAmelCase      :							Dict       ,				)		-> int:
		"""simple docstring"""







		super().__init__(
		    unk_token=_lowerCAmelCase       ,				pad_token=_lowerCAmelCase       ,				bos_token=_lowerCAmelCase       ,				eos_token=_lowerCAmelCase       ,				do_clean_text=_lowerCAmelCase       ,				**_lowerCAmelCase       ,				)
		if not os.path.isfile(_lowerCAmelCase			):
			raise ValueError(
			    F'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
			    """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""			)
		if not os.path.isfile(_lowerCAmelCase			):
			raise ValueError(
			    F'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
			    """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""			)
		__lowercase             =  do_clean_text
		__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  load_vocab_and_emoji(_lowerCAmelCase       ,				_lowerCAmelCase			)
		__lowercase             =  SubWordJapaneseTokenizer(
		    vocab=self.vocab       ,				ids_to_tokens=self.ids_to_tokens       ,				emoji=self.emoji			)







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







		return len(self.raw_vocab			)







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







		return dict(self.raw_vocab       ,				**self.added_tokens_encoder			)







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







		return self.subword_tokenizer.tokenize(_lowerCAmelCase       ,				clean=self.do_clean_text			)







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







		return self.vocab.get(_lowerCAmelCase       ,				self.vocab.get(self.unk_token			)			)







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







		return self.subword_tokenizer.convert_id_to_token(_lowerCAmelCase			)







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







		__lowercase             =  """""".join(_lowerCAmelCase			).strip()
		return out_string







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







		__lowercase             =  []
		for is_user, text in conversation.iter_texts():
			input_ids.extend(self.encode(_lowerCAmelCase       ,				add_special_tokens=_lowerCAmelCase			) + [self.eos_token_id]			)

		if len(_lowerCAmelCase			) > self.model_max_length:
			__lowercase             =  input_ids[-self.model_max_length :]
		return input_ids







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







		__lowercase             =  0
		if os.path.isdir(_lowerCAmelCase			):
			__lowercase             =  os.path.join(
			    _lowerCAmelCase       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)
			__lowercase             =  os.path.join(
			    _lowerCAmelCase       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]			)
		else:
			__lowercase             =  (
			    (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
			)
			__lowercase             =  (
			    (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
			)
		with open(_lowerCAmelCase       ,				"""w"""       ,				encoding="""utf-8"""			) as writer:
			for token_index, token in self.ids_to_tokens.items():
				if index != token_index:
					logger.warning(
					    F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
					    """ Please check that the vocabulary is not corrupted!"""			)
					__lowercase             =  token_index
				writer.write(""",""".join(_lowerCAmelCase			) + """\n"""			)
				index += 1
		with open(_lowerCAmelCase       ,				"""w"""       ,				encoding="""utf-8"""			) as writer:
			json.dump(self.emoji       ,				_lowerCAmelCase			)
		return vocab_file, emoji_file


class        __UpperCamelCase    (     _lowerCAmelCase   ):







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







		__lowercase             =  vocab  # same as swe
		__lowercase             =  ids_to_tokens  # same as bpe
		__lowercase             =  emoji
		__lowercase             =  np.max([len(_lowerCAmelCase			) for w in self.vocab.keys()]			)
		__lowercase             =  re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"""			)
		__lowercase             =  re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"""			)
		__lowercase             =  re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"""			)
		__lowercase             =  re.compile(
		    r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""			)
		__lowercase             =  re.compile(
		    r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""			)
		__lowercase             =  re.compile(
		    r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"""			)
		__lowercase             =  """─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
		__lowercase             =  """▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
		__lowercase             =  str.maketrans({k: """<BLOCK>""" for k in keisen + blocks}			)







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







		return len(self.ids_to_tokens			)







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







		__lowercase             =  self.content_repattera.sub("""<URL>"""       ,				_lowerCAmelCase			)
		__lowercase             =  self.content_repattera.sub("""<EMAIL>"""       ,				_lowerCAmelCase			)
		__lowercase             =  self.content_repattera.sub("""<TEL>"""       ,				_lowerCAmelCase			)
		__lowercase             =  self.content_repattera.sub("""<DATE>"""       ,				_lowerCAmelCase			)
		__lowercase             =  self.content_repattera.sub("""<DATE>"""       ,				_lowerCAmelCase			)
		__lowercase             =  self.content_repattera.sub("""<PRICE>"""       ,				_lowerCAmelCase			)
		__lowercase             =  content.translate(self.content_transa			)
		while "<BLOCK><BLOCK>" in content:
			__lowercase             =  content.replace("""<BLOCK><BLOCK>"""       ,				"""<BLOCK>"""			)
		return content







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







		__lowercase             =  text.replace(""" """       ,				"""<SP>"""			)
		__lowercase             =  text.replace("""　"""       ,				"""<SP>"""			)
		__lowercase             =  text.replace("""\r\n"""       ,				"""<BR>"""			)
		__lowercase             =  text.replace("""\n"""       ,				"""<BR>"""			)
		__lowercase             =  text.replace("""\r"""       ,				"""<BR>"""			)
		__lowercase             =  text.replace("""\t"""       ,				"""<TAB>"""			)
		__lowercase             =  text.replace("""—"""       ,				"""ー"""			)
		__lowercase             =  text.replace("""−"""       ,				"""ー"""			)
		for k, v in self.emoji["emoji"].items():
			if k in text:
				__lowercase             =  text.replace(_lowerCAmelCase       ,				_lowerCAmelCase			)
		if clean:
			__lowercase             =  self.clean_text(_lowerCAmelCase			)

		def check_simbol(_lowerCAmelCase      :							Union[str, Any]			):
			__lowercase             =  x.encode()
			if len(_lowerCAmelCase			) == 1 and len(_lowerCAmelCase			) == 2:
				__lowercase             =  (int(e[0]			) << 8) + int(e[1]			)
				if (
				    (c >= 0Xc2a1 and c <= 0Xc2bf)
				    or (c >= 0Xc780 and c <= 0Xc783)
				    or (c >= 0Xcab9 and c <= 0Xcbbf)
				    or (c >= 0Xcc80 and c <= 0Xcda2)
				):
					return True
			return False

		def checkuae(_lowerCAmelCase      :							Tuple			):
			__lowercase             =  x.encode()
			if len(_lowerCAmelCase			) == 1 and len(_lowerCAmelCase			) == 3:
				__lowercase             =  (int(e[0]			) << 16) + (int(e[1]			) << 8) + int(e[2]			)
				if c >= 0Xe2_8080 and c <= 0Xe2_b07f:
					return True
			return False

		__lowercase             =  0
		__lowercase             =  []
		while pos < len(_lowerCAmelCase			):
			__lowercase             =  min(len(_lowerCAmelCase			)       ,				pos + self.maxlen + 1			) if text[pos] == """<""" else pos + 3
			__lowercase             =  []  # (token_id, token, pos)
			for e in range(_lowerCAmelCase       ,				_lowerCAmelCase       ,				-1			):
				__lowercase             =  text[pos:e]
				if wd in self.vocab:
					if wd[0] == "<" and len(_lowerCAmelCase			) > 2:
						__lowercase             =  [(self.vocab[wd], wd, e)]
						break
					else:
						candidates.append((self.vocab[wd], wd, e)			)
			if len(_lowerCAmelCase			) > 0:
				# the smallest token_id is adopted
				__lowercase       ,						__lowercase       ,						__lowercase             =  sorted(_lowerCAmelCase       ,				key=lambda _lowerCAmelCase			: x[0]			)[0]
				result.append(_lowerCAmelCase			)
				__lowercase             =  e
			else:
				__lowercase             =  pos + 1
				__lowercase             =  text[pos:end]
				if check_simbol(_lowerCAmelCase			):
					result.append("""<KIGOU>"""			)
				elif checkuae(_lowerCAmelCase			):
					result.append("""<U2000U2BFF>"""			)
				else:
					for i in wd.encode("""utf-8"""			):
						result.append("""<|byte%d|>""" % i			)
				__lowercase             =  end
		return result







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







		__lowercase             =  []
		__lowercase             =  []
		__lowercase             =  self.ids_to_tokens[index][0]
		if word[:6] == "<|byte" and word[-2:] == "|>":
			byte_tokens.append(int(word[6:-2]			)			)
		else:
			if len(_lowerCAmelCase			) > 0:
				words.append(bytearray(_lowerCAmelCase			).decode("""utf-8"""       ,				errors="""replace"""			)			)
				__lowercase             =  []
			if word[:7] == "<|emoji" and word[-2:] == "|>":
				words.append(self.emoji["""emoji_inv"""][word]			)
			elif word == "<SP>":
				words.append(""" """			)
			elif word == "<BR>":
				words.append(_lowerCAmelCase			)
			elif word == "<TAB>":
				words.append("""\t"""			)
			elif word == "<BLOCK>":
				words.append("""▀"""			)
			elif word == "<KIGOU>":
				words.append("""ǀ"""			)
			elif word == "<U2000U2BFF>":
				words.append("""‖"""			)
			else:
				words.append(_lowerCAmelCase			)
		if len(_lowerCAmelCase			) > 0:
			words.append(bytearray(_lowerCAmelCase			).decode("""utf-8"""       ,				errors="""replace"""			)			)
		__lowercase             =  """""".join(_lowerCAmelCase			)
		return text









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			)			)

