



"""simple docstring"""


import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				from transformers.models.electra.modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				)







class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:Tuple  ,    lowercase_					:Union[str, Any]=13  ,    lowercase_					:int=7  ,    lowercase_					:Optional[int]=True  ,    lowercase_					:Any=True  ,    lowercase_					:Tuple=True  ,    lowercase_					:List[str]=True  ,    lowercase_					:Any=99  ,    lowercase_					:Optional[int]=24  ,    lowercase_					:Optional[int]=32  ,    lowercase_					:int=5  ,    lowercase_					:str=4  ,    lowercase_					:Tuple=37  ,    lowercase_					:Optional[int]="gelu"  ,    lowercase_					:Union[str, Any]=0.1  ,    lowercase_					:Dict=0.1  ,    lowercase_					:Optional[int]=5_12  ,    lowercase_					:Optional[int]=16  ,    lowercase_					:List[Any]=2  ,    lowercase_					:str=0.02  ,    lowercase_					:Optional[Any]=4  ,    )							->     Optional[int]:
												UpperCAmelCase			 =					parent
												UpperCAmelCase			 =					batch_size
												UpperCAmelCase			 =					seq_length
												UpperCAmelCase			 =					is_training
												UpperCAmelCase			 =					use_attention_mask
												UpperCAmelCase			 =					use_token_type_ids
												UpperCAmelCase			 =					use_labels
												UpperCAmelCase			 =					vocab_size
												UpperCAmelCase			 =					hidden_size
												UpperCAmelCase			 =					embedding_size
												UpperCAmelCase			 =					num_hidden_layers
												UpperCAmelCase			 =					num_attention_heads
												UpperCAmelCase			 =					intermediate_size
												UpperCAmelCase			 =					hidden_act
												UpperCAmelCase			 =					hidden_dropout_prob
												UpperCAmelCase			 =					attention_probs_dropout_prob
												UpperCAmelCase			 =					max_position_embeddings
												UpperCAmelCase			 =					type_vocab_size
												UpperCAmelCase			 =					type_sequence_label_size
												UpperCAmelCase			 =					initializer_range
												UpperCAmelCase			 =					num_choices






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     List[str]:
												UpperCAmelCase			 =					ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size   )

												UpperCAmelCase			 =					None
												if self.use_attention_mask:
																		UpperCAmelCase			 =					random_attention_mask([self.batch_size, self.seq_length]   )

												UpperCAmelCase			 =					None
												if self.use_token_type_ids:
																		UpperCAmelCase			 =					ids_tensor([self.batch_size, self.seq_length]  ,    self.type_vocab_size   )

												UpperCAmelCase			 =					ElectraConfig(
												    vocab_size=self.vocab_size  ,    hidden_size=self.hidden_size  ,    embedding_size=self.embedding_size  ,    num_hidden_layers=self.num_hidden_layers  ,    num_attention_heads=self.num_attention_heads  ,    intermediate_size=self.intermediate_size  ,    hidden_act=self.hidden_act  ,    hidden_dropout_prob=self.hidden_dropout_prob  ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,    max_position_embeddings=self.max_position_embeddings  ,    type_vocab_size=self.type_vocab_size  ,    initializer_range=self.initializer_range  ,    )

												return config, input_ids, token_type_ids, attention_mask






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.prepare_config_and_inputs()
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					config_and_inputs
												UpperCAmelCase			 =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
												return config, inputs_dict







@require_flax
class  A_     (	SCREAMING_SNAKE_CASE_      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				True

						__UpperCamelCase													=				(
						    (
						        FlaxElectraModel,
						        FlaxElectraForCausalLM,
						        FlaxElectraForMaskedLM,
						        FlaxElectraForPreTraining,
						        FlaxElectraForTokenClassification,
						        FlaxElectraForQuestionAnswering,
						        FlaxElectraForMultipleChoice,
						        FlaxElectraForSequenceClassification,
						    )
						    if is_flax_available()
						    else ()
						)






						def       UpperCAmelCase__	(   self					:List[str]   )							->     str:
												UpperCAmelCase			 =					FlaxElectraModelTester(self   )






						@slow
						def       UpperCAmelCase__	(   self					:Any   )							->     Optional[Any]:
												for model_class_name in self.all_model_classes:
																		if model_class_name == FlaxElectraForMaskedLM:
																								UpperCAmelCase			 =					model_class_name.from_pretrained('google/electra-small-generator'   )
																		else:
																								UpperCAmelCase			 =					model_class_name.from_pretrained('google/electra-small-discriminator'   )
																		UpperCAmelCase			 =					model(np.ones((1, 1)   )   )
																		self.assertIsNotNone(lowercase_   )










"""simple docstring"""


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_								=			{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_								=			{
    """openai/clip-vit-base-patch32""": 77,
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask"""]
						__UpperCamelCase													=				CLIPTokenizer






						def __init__(   self					:int  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:int=None  ,    lowercase_					:Optional[Any]=None  ,    lowercase_					:Dict="<|endoftext|>"  ,    lowercase_					:List[Any]="<|startoftext|>"  ,    lowercase_					:Any="<|endoftext|>"  ,    lowercase_					:List[str]="<|endoftext|>"  ,    **lowercase_					:Tuple  ,    )							->     List[Any]:
												super().__init__(
												    lowercase_  ,    lowercase_  ,    tokenizer_file=lowercase_  ,    unk_token=lowercase_  ,    bos_token=lowercase_  ,    eos_token=lowercase_  ,    pad_token=lowercase_  ,    **lowercase_  ,    )

												if not isinstance(self.backend_tokenizer.pre_tokenizer  ,    pre_tokenizers.Sequence   ):
																		raise ValueError(
																		    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
																		    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
																		    ' to be compatible with this version.The easiest way to do so is'
																		    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
																		    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
																		    ' transformers.'   )

												self._wrap_decode_method_backend_tokenizer()






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[Any]:
												UpperCAmelCase			 =					self.backend_tokenizer.decode

												def new_decode_method(*lowercase_					:List[Any]  ,    **lowercase_					:Optional[Any]   ):
																		UpperCAmelCase			 =					orig_decode_method(*lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					text.replace(self.backend_tokenizer.model.end_of_word_suffix  ,    ' '   ).strip()
																		return text

												UpperCAmelCase			 =					new_decode_method






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return bos_token + token_ids_a + eos_token
												return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return len(bos_token + token_ids_a + eos_token   ) * [0]
												return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )





