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 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							str=None ,	UpperCAmelCase__       :							Optional[int]=None					):
					if attention_mask is None:
										SCREAMING_SNAKE_CASE           =    tf.cast(tf.math.not_equal(UpperCAmelCase__ ,	config.pad_token_id					) ,	tf.inta					)
					return {"input_ids": input_ids, "attention_mask": attention_mask}


@require_tf
class   lowercase      :
							lowercase__  :    List[str]									=							OPTConfig
							lowercase__  :    Union[str, Any]									=							{}
							lowercase__  :    List[str]									=							"""gelu"""




							def __init__(  self				:   Any		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   List[str]=13		,							_UpperCamelCase				:   Optional[Any]=7		,							_UpperCamelCase				:   List[Any]=True		,							_UpperCamelCase				:   List[str]=False		,							_UpperCamelCase				:   List[Any]=99		,							_UpperCamelCase				:   List[str]=16		,							_UpperCamelCase				:   str=2		,							_UpperCamelCase				:   List[str]=4		,							_UpperCamelCase				:   str=4		,							_UpperCamelCase				:   Dict="gelu"		,							_UpperCamelCase				:   List[str]=0.1		,							_UpperCamelCase				:   Union[str, Any]=0.1		,							_UpperCamelCase				:   Any=20		,							_UpperCamelCase				:   Optional[int]=2		,							_UpperCamelCase				:   Dict=1		,							_UpperCamelCase				:   Optional[Any]=0		,							_UpperCamelCase				:   Union[str, Any]=16		,							_UpperCamelCase				:   List[Any]=16		,							)   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    seq_length
												SCREAMING_SNAKE_CASE           =    is_training
												SCREAMING_SNAKE_CASE           =    use_labels
												SCREAMING_SNAKE_CASE           =    vocab_size
												SCREAMING_SNAKE_CASE           =    hidden_size
												SCREAMING_SNAKE_CASE           =    num_hidden_layers
												SCREAMING_SNAKE_CASE           =    num_attention_heads
												SCREAMING_SNAKE_CASE           =    intermediate_size
												SCREAMING_SNAKE_CASE           =    hidden_act
												SCREAMING_SNAKE_CASE           =    hidden_dropout_prob
												SCREAMING_SNAKE_CASE           =    attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE           =    max_position_embeddings
												SCREAMING_SNAKE_CASE           =    eos_token_id
												SCREAMING_SNAKE_CASE           =    pad_token_id
												SCREAMING_SNAKE_CASE           =    bos_token_id
												SCREAMING_SNAKE_CASE           =    embed_dim
												SCREAMING_SNAKE_CASE           =    word_embed_proj_dim
												SCREAMING_SNAKE_CASE           =    False




							def 		__snake_case(  self				:   List[str] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    ids_tensor([self.batch_size, self.seq_length - 1]		,							self.vocab_size )
												SCREAMING_SNAKE_CASE           =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )		,							1 )
												SCREAMING_SNAKE_CASE           =    tf.concat([input_ids, eos_tensor]		,							axis=1 )

												SCREAMING_SNAKE_CASE           =    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=_UpperCamelCase		,							**self.config_updates		,							)
												SCREAMING_SNAKE_CASE           =    prepare_opt_inputs_dict(_UpperCamelCase		,							_UpperCamelCase )
												return config, inputs_dict





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Any )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFOPTModel(config=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    inputs_dict["input_ids"]

												SCREAMING_SNAKE_CASE           =    input_ids[:1, :]
												SCREAMING_SNAKE_CASE           =    inputs_dict["attention_mask"][:1, :]
												SCREAMING_SNAKE_CASE           =    1

												# first forward pass
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							use_cache=_UpperCamelCase )

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    outputs.to_tuple()

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

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

												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase )[0]
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							attention_mask=_UpperCamelCase		,							past_key_values=_UpperCamelCase )[0]

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

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

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


@require_tf
class   lowercase      (       a			,    a			,    unittest.TestCase       ):
							lowercase__  :    List[str]									=							(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
							lowercase__  :    List[Any]									=							(TFOPTForCausalLM,) if is_tf_available() else ()
							lowercase__  :    Optional[Any]									=							(
							    {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
							)
							lowercase__  :    str									=							False
							lowercase__  :    List[str]									=							False
							lowercase__  :    Any									=							False
							lowercase__  :    str									=							10




							def 		__snake_case(  self				:   str )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFOPTModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(self		,							config_class=_UpperCamelCase )




							def 		__snake_case(  self				:   int )   ->			Any:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							def 		__snake_case(  self				:   Any )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase )





							def 		__snake_case(  self				:   Dict )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

												def _get_word_embedding_weight(_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any] ):
																	if hasattr(_UpperCamelCase		,							"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(_UpperCamelCase		,							"weight" ):
																											return embedding_layer.weight
																						else:
																											return None

												for model_class in self.all_model_classes:
																	for size in [config.vocab_size - 10, config.vocab_size + 10]:
																						# build the embeddings
																						SCREAMING_SNAKE_CASE           =    model_class(config=_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    _get_word_embedding_weight(_UpperCamelCase		,							model.get_input_embeddings() )
																						SCREAMING_SNAKE_CASE           =    _get_word_embedding_weight(_UpperCamelCase		,							model.get_output_embeddings() )

																						# reshape the embeddings
																						model.resize_token_embeddings(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    _get_word_embedding_weight(_UpperCamelCase		,							model.get_input_embeddings() )
																						SCREAMING_SNAKE_CASE           =    _get_word_embedding_weight(_UpperCamelCase		,							model.get_output_embeddings() )

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

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

																						# check that weights remain the same after resizing
																						SCREAMING_SNAKE_CASE           =    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:
																																SCREAMING_SNAKE_CASE           =    False
																						self.assertTrue(_UpperCamelCase )

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

																											SCREAMING_SNAKE_CASE           =    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:
																																					SCREAMING_SNAKE_CASE           =    False
																											self.assertTrue(_UpperCamelCase )





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
					return tf.constant(UpperCAmelCase__ ,	dtype=tf.intaa					)


@require_tf
class   lowercase      (       unittest.TestCase       ):
							lowercase__  :    int									=							99




							def 		__snake_case(  self				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tf.ones((4, 1)		,							dtype=tf.intaa ) * 2
												SCREAMING_SNAKE_CASE           =    tf.concat([ids_tensor((4, 6)		,							self.vocab_size - 3 ) + 3, eos_column_vector]		,							axis=1 )
												SCREAMING_SNAKE_CASE           =    input_ids.shape[0]
												SCREAMING_SNAKE_CASE           =    OPTConfig(
												    vocab_size=self.vocab_size		,							hidden_size=24		,							num_hidden_layers=2		,							num_attention_heads=2		,							ffn_dim=32		,							max_position_embeddings=48		,							eos_token_id=2		,							pad_token_id=1		,							bos_token_id=0		,							)
												return config, input_ids, batch_size


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




							@slow
							def 		__snake_case(  self				:   Optional[Any] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFOPTModel.from_pretrained("facebook/opt-350m" )
												SCREAMING_SNAKE_CASE           =    _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
												SCREAMING_SNAKE_CASE           =    tf.not_equal(_UpperCamelCase		,							model.config.pad_token_id )
												with tf.GradientTape():
																	SCREAMING_SNAKE_CASE           =    model(input_ids=_UpperCamelCase		,							attention_mask=_UpperCamelCase ).last_hidden_state
												SCREAMING_SNAKE_CASE           =    (1, 11, 512)
												self.assertEqual(output.shape		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tf.constant(
												    [[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]] )
												self.assertTrue(np.allclose(output[:, :3, :3]		,							_UpperCamelCase		,							atol=4e-3 ) )

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


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




							def 		__snake_case(  self				:   Any )   ->			Dict:



												'''simple docstring'''

												super().setUp()
												SCREAMING_SNAKE_CASE           =    "facebook/opt-350m"





							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    TFOPTForCausalLM.from_pretrained(self.path_model )
												SCREAMING_SNAKE_CASE           =    GPTaTokenizer.from_pretrained(self.path_model )

												SCREAMING_SNAKE_CASE           =    [
												    "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
												SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase		,							return_tensors="tf"		,							padding=_UpperCamelCase		,							add_special_tokens=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tf.math.reduce_mean(model(inputs.input_ids		,							attention_mask=inputs.attention_mask )[0]		,							axis=-1 )
												SCREAMING_SNAKE_CASE           =    tf.constant(
												    [
												        [1.3_8_5_1, -13.8_923, -10.5_229, -10.7_533, -0.2_3_0_9, -10.2_384, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
												        [-4.7_0_7_3, -10.6_276, -3.9_4_1_5, -21.5_242, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
												        [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -14.1_650, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
												        [6.4_7_8_3, -1.9_9_1_3, -10.7_926, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
												    ] )
												self.assertTrue(np.allclose(_UpperCamelCase		,							_UpperCamelCase		,							atol=1e-4 ) )

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


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




							@property
							def 		__snake_case(  self				:   List[str] )   ->			str:



												'''simple docstring'''

												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 		__snake_case(  self				:   Tuple )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    "facebook/opt-125m"

												SCREAMING_SNAKE_CASE           =    [
												    "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",
												]

												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    GPTaTokenizer.from_pretrained(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    TFOPTForCausalLM.from_pretrained(_UpperCamelCase )

												for prompt in self.prompts:
																	SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase		,							return_tensors="tf" ).input_ids

																	SCREAMING_SNAKE_CASE           =    model.generate(_UpperCamelCase		,							max_length=10 )

																	SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase		,							skip_special_tokens=_UpperCamelCase )
																	predicted_outputs += generated_string

												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   List[str] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    "facebook/opt-350m"

												SCREAMING_SNAKE_CASE           =    GPTaTokenizer.from_pretrained(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    TFOPTForCausalLM.from_pretrained(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "left"

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

												SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase		,							return_tensors="tf"		,							padding=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    inputs["input_ids"]

												SCREAMING_SNAKE_CASE           =    model.generate(input_ids=_UpperCamelCase		,							attention_mask=inputs["attention_mask"] )

												SCREAMING_SNAKE_CASE           =    tokenizer(sentences[0]		,							return_tensors="tf" ).input_ids
												SCREAMING_SNAKE_CASE           =    model.generate(input_ids=_UpperCamelCase )

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

												SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase		,							skip_special_tokens=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tokenizer.decode(output_non_padded[0]		,							skip_special_tokens=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tokenizer.decode(output_padded[0]		,							skip_special_tokens=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    [
												    "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(_UpperCamelCase		,							_UpperCamelCase )
												self.assertListEqual(_UpperCamelCase		,							[non_padded_sentence, padded_sentence] )





							def 		__snake_case(  self				:   List[str] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    "facebook/opt-350m"

												SCREAMING_SNAKE_CASE           =    [
												    "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",
												]

												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    GPTaTokenizer.from_pretrained(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    TFOPTForCausalLM.from_pretrained(_UpperCamelCase )

												for prompt in self.prompts:
																	SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase		,							return_tensors="tf" ).input_ids

																	SCREAMING_SNAKE_CASE           =    model.generate(_UpperCamelCase		,							max_length=10 )

																	SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase		,							skip_special_tokens=_UpperCamelCase )
																	predicted_outputs += generated_string

												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase )





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor


@require_vision
class   lowercase      (       unittest.TestCase       ):




							def 		__snake_case(  self				:   Tuple )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE           =    [
												    "[UNK]",
												    "[CLS]",
												    "[SEP]",
												    "[PAD]",
												    "[MASK]",
												    "的",
												    "价",
												    "格",
												    "是",
												    "15",
												    "便",
												    "alex",
												    "##andra",
												    "，",
												    "。",
												    "-",
												    "t",
												    "shirt",
												]
												SCREAMING_SNAKE_CASE           =    os.path.join(self.tmpdirname		,							VOCAB_FILES_NAMES["vocab_file"] )
												with open(self.vocab_file		,							"w"		,							encoding="utf-8" ) as vocab_writer:
																	vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

												SCREAMING_SNAKE_CASE           =    {
												    "do_resize": True,
												    "size": {"height": 224, "width": 224},
												    "do_center_crop": True,
												    "crop_size": {"height": 18, "width": 18},
												    "do_normalize": True,
												    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
												    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
												    "do_convert_rgb": True,
												}
												SCREAMING_SNAKE_CASE           =    os.path.join(self.tmpdirname		,							_UpperCamelCase )
												with open(self.image_processor_file		,							"w"		,							encoding="utf-8" ) as fp:
																	json.dump(_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any]		,							**_UpperCamelCase				:   str )   ->			int:



												'''simple docstring'''

												return BertTokenizer.from_pretrained(self.tmpdirname		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Tuple )   ->			str:



												'''simple docstring'''

												return BertTokenizerFast.from_pretrained(self.tmpdirname		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							**_UpperCamelCase				:   Optional[Any] )   ->			Any:



												'''simple docstring'''

												return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[Any] )   ->			int:



												'''simple docstring'''

												shutil.rmtree(self.tmpdirname )




							def 		__snake_case(  self				:   Dict )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [np.random.randint(255		,							size=(3, 30, 400)		,							dtype=np.uinta )]

												SCREAMING_SNAKE_CASE           =    [Image.fromarray(np.moveaxis(_UpperCamelCase		,							0		,							-1 ) ) for x in image_inputs]

												return image_inputs




							def 		__snake_case(  self				:   List[str] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()
												SCREAMING_SNAKE_CASE           =    self.get_rust_tokenizer()
												SCREAMING_SNAKE_CASE           =    self.get_image_processor()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )
												processor_slow.save_pretrained(self.tmpdirname )
												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,							use_fast=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )
												processor_fast.save_pretrained(self.tmpdirname )
												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname )

												self.assertEqual(processor_slow.tokenizer.get_vocab()		,							tokenizer_slow.get_vocab() )
												self.assertEqual(processor_fast.tokenizer.get_vocab()		,							tokenizer_fast.get_vocab() )
												self.assertEqual(tokenizer_slow.get_vocab()		,							tokenizer_fast.get_vocab() )
												self.assertIsInstance(processor_slow.tokenizer		,							_UpperCamelCase )
												self.assertIsInstance(processor_fast.tokenizer		,							_UpperCamelCase )

												self.assertEqual(processor_slow.image_processor.to_json_string()		,							image_processor.to_json_string() )
												self.assertEqual(processor_fast.image_processor.to_json_string()		,							image_processor.to_json_string() )
												self.assertIsInstance(processor_slow.image_processor		,							_UpperCamelCase )
												self.assertIsInstance(processor_fast.image_processor		,							_UpperCamelCase )




							def 		__snake_case(  self				:   str )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,							image_processor=self.get_image_processor() )
												processor.save_pretrained(self.tmpdirname )

												SCREAMING_SNAKE_CASE           =    self.get_tokenizer(cls_token="(CLS)"		,							sep_token="(SEP)" )
												SCREAMING_SNAKE_CASE           =    self.get_image_processor(do_normalize=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor.from_pretrained(
												    self.tmpdirname		,							cls_token="(CLS)"		,							sep_token="(SEP)"		,							do_normalize=_UpperCamelCase )

												self.assertEqual(processor.tokenizer.get_vocab()		,							tokenizer_add_kwargs.get_vocab() )
												self.assertIsInstance(processor.tokenizer		,							_UpperCamelCase )

												self.assertEqual(processor.image_processor.to_json_string()		,							image_processor_add_kwargs.to_json_string() )
												self.assertIsInstance(processor.image_processor		,							_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    image_processor(_UpperCamelCase		,							return_tensors="np" )
												SCREAMING_SNAKE_CASE           =    processor(images=_UpperCamelCase		,							return_tensors="np" )

												for key in input_feat_extract.keys():
																	self.assertAlmostEqual(input_feat_extract[key].sum()		,							input_processor[key].sum()		,							delta=1e-2 )




							def 		__snake_case(  self				:   List[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "Alexandra，T-shirt的价格是15便士。"

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    tokenizer(_UpperCamelCase )

												for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]		,							encoded_processor[key] )




							def 		__snake_case(  self				:   Optional[Any] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "Alexandra，T-shirt的价格是15便士。"
												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase		,							images=_UpperCamelCase )

												self.assertListEqual(list(inputs.keys() )		,							["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )

												# test if it raises when no input is passed
												with pytest.raises(_UpperCamelCase ):
																	processor()




							def 		__snake_case(  self				:   Optional[Any] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												SCREAMING_SNAKE_CASE           =    processor.batch_decode(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(_UpperCamelCase )

												self.assertListEqual(_UpperCamelCase		,							_UpperCamelCase )





							def 		__snake_case(  self				:   int )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.get_image_processor()
												SCREAMING_SNAKE_CASE           =    self.get_tokenizer()

												SCREAMING_SNAKE_CASE           =    ChineseCLIPProcessor(tokenizer=_UpperCamelCase		,							image_processor=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    "Alexandra，T-shirt的价格是15便士。"
												SCREAMING_SNAKE_CASE           =    self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE           =    processor(text=_UpperCamelCase		,							images=_UpperCamelCase )

												self.assertListEqual(list(inputs.keys() )		,							processor.model_input_names )




