



from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
					import tensorflow as tf

					from transformers import TFCvtForImageClassification, TFCvtModel
					from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor




class 			snake_case_						(						_lowerCAmelCase				):






							def snake_case_  (   self      ):
									a_     :      str 						=     self.config_class(**self.inputs_dict      )
									self.parent.assertTrue(hasattr(_lowerCAmelCase			,     "embed_dim"      )      )
									self.parent.assertTrue(hasattr(_lowerCAmelCase			,     "num_heads"      )      )




class 			snake_case_						:






							def __init__(   self			,     a_			,     a_=1_3			,     a_=6_4			,     a_=3			,     a_=[1_6, 4_8, 9_6]			,     a_=[1, 3, 6]			,     a_=[1, 2, 1_0]			,     a_=[7, 3, 3]			,     a_=[4, 2, 2]			,     a_=[2, 1, 1]			,     a_=[2, 2, 2]			,     a_=[False, False, True]			,     a_=[0.0, 0.0, 0.0]			,     a_=0.02			,     a_=1e-12			,     a_=True			,     a_=True			,     a_=2			,     ):
									a_     :      Tuple 						=     parent
									a_     :      List[Any] 						=     batch_size
									a_     :      str 						=     image_size
									a_     :      int 						=     patch_sizes
									a_     :      Union[str, Any] 						=     patch_stride
									a_     :      int 						=     patch_padding
									a_     :      Optional[Any] 						=     is_training
									a_     :      Tuple 						=     use_labels
									a_     :      Dict 						=     num_labels
									a_     :      Dict 						=     num_channels
									a_     :      List[Any] 						=     embed_dim
									a_     :      List[Any] 						=     num_heads
									a_     :      Optional[int] 						=     stride_kv
									a_     :      str 						=     depth
									a_     :      Optional[int] 						=     cls_token
									a_     :      Tuple 						=     attention_drop_rate
									a_     :      Dict 						=     initializer_range
									a_     :      List[str] 						=     layer_norm_eps






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									a_     :      str 						=     None
									if self.use_labels:
											# create a random int32 tensor of given shape
											a_     :      Optional[Any] 						=     ids_tensor([self.batch_size]			,     self.num_labels      )

									a_     :      List[str] 						=     self.get_config()
									return config, pixel_values, labels






							def snake_case_  (   self      ):
									return CvtConfig(
									    image_size=self.image_size			,     num_labels=self.num_labels			,     num_channels=self.num_channels			,     embed_dim=self.embed_dim			,     num_heads=self.num_heads			,     patch_sizes=self.patch_sizes			,     patch_padding=self.patch_padding			,     patch_stride=self.patch_stride			,     stride_kv=self.stride_kv			,     depth=self.depth			,     cls_token=self.cls_token			,     attention_drop_rate=self.attention_drop_rate			,     initializer_range=self.initializer_range			,     )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      Optional[Any] 						=     TFCvtModel(config=_lowerCAmelCase      )
									a_     :      int 						=     model(_lowerCAmelCase			,     training=_lowerCAmelCase      )
									a_     :      Tuple 						=     (self.image_size, self.image_size)
									a_						,				a_     :      Optional[Any] 						=     image_size[0], image_size[1]
									for i in range(len(self.depth      )      ):
											a_     :      Any 						=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1      )
											a_     :      List[str] 						=     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1      )
									self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.embed_dim[-1], height, width)      )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      Optional[Any] 						=     self.num_labels
									a_     :      int 						=     TFCvtForImageClassification(_lowerCAmelCase      )
									a_     :      Optional[int] 						=     model(_lowerCAmelCase			,     labels=_lowerCAmelCase			,     training=_lowerCAmelCase      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.prepare_config_and_inputs()
									a_						,				a_						,				a_     :      Optional[int] 						=     config_and_inputs
									a_     :      Union[str, Any] 						=     {"pixel_values": pixel_values}
									return config, inputs_dict




@require_tf
class 			snake_case_						(						_lowerCAmelCase     ,_lowerCAmelCase     ,unittest.TestCase				):
							__lowerCAmelCase    					=					(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
							__lowerCAmelCase    					=					(
							    {'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
							    if is_tf_available()
							    else {}
							)
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     TFCvtModelTester(self      )
									a_     :      Dict 						=     TFCvtConfigTester(self			,     config_class=_lowerCAmelCase			,     has_text_modality=_lowerCAmelCase			,     hidden_size=3_7      )






							def snake_case_  (   self      ):
									self.config_tester.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()






							@unittest.skip(reason="Cvt does not output attentions"      )
							def snake_case_  (   self      ):
									pass






							@unittest.skip(reason="Cvt does not use inputs_embeds"      )
							def snake_case_  (   self      ):
									pass






							@unittest.skip(reason="Cvt does not support input and output embeddings"      )
							def snake_case_  (   self      ):
									pass






							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("GPU"      )      ) == 0			,     reason="TF does not support backprop for grouped convolutions on CPU."			,     )
							def snake_case_  (   self      ):
									super().test_dataset_conversion()






							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("GPU"      )      ) == 0			,     reason="TF does not support backprop for grouped convolutions on CPU."			,     )
							@slow
							def snake_case_  (   self      ):
									super().test_keras_fit()






							@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"      )
							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     tf.keras.mixed_precision.Policy("mixed_float16"      )
									tf.keras.mixed_precision.set_global_policy(_lowerCAmelCase      )
									super().test_keras_fit()
									tf.keras.mixed_precision.set_global_policy("float32"      )






							def snake_case_  (   self      ):
									a_						,				a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_     :      Tuple 						=     model_class(_lowerCAmelCase      )
											a_     :      List[Any] 						=     inspect.signature(model.call      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											a_     :      int 						=     [*signature.parameters.keys()]

											a_     :      Optional[Any] 						=     ["pixel_values"]
											self.assertListEqual(arg_names[:1]			,     _lowerCAmelCase      )






							def snake_case_  (   self      ):







									def check_hidden_states_output(a_			,     a_			,     a_      ):
											a_     :      int 						=     model_class(_lowerCAmelCase      )

											a_     :      List[str] 						=     model(**self._prepare_for_class(_lowerCAmelCase			,     _lowerCAmelCase      )      )
											a_     :      Dict 						=     outputs.hidden_states

											a_     :      List[str] 						=     len(self.model_tester.depth      )
											self.assertEqual(len(_lowerCAmelCase      )			,     _lowerCAmelCase      )

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]      )			,     [
											        self.model_tester.embed_dim[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]			,     )

									a_						,				a_     :      Optional[int] 						=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_     :      Union[str, Any] 						=     True
											check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase      )

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											a_     :      int 						=     True

											check_hidden_states_output(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowerCAmelCase      )






							def snake_case_  (   self      ):
									a_     :      str 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase      )






							@slow
							def snake_case_  (   self      ):







									for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_     :      Dict 						=     TFCvtModel.from_pretrained(_lowerCAmelCase      )
											self.assertIsNotNone(_lowerCAmelCase      )



def        lowerCAmelCase_						(							)			->      List[Any]:
		a_     :      Dict 						=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
		return image




@require_tf
@require_vision
class 			snake_case_						(						unittest.TestCase				):






							@cached_property
							def snake_case_  (   self      ):
									return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )






							@slow
							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

									a_     :      Union[str, Any] 						=     self.default_image_processor
									a_     :      Optional[int] 						=     prepare_img()
									a_     :      Optional[int] 						=     image_processor(images=_lowerCAmelCase			,     return_tensors="tf"      )

									# forward pass
									a_     :      Union[str, Any] 						=     model(**_lowerCAmelCase      )

									# verify the logits
									a_     :      List[Any] 						=     tf.TensorShape((1, 1_0_0_0)      )
									self.assertEqual(outputs.logits.shape			,     _lowerCAmelCase      )

									a_     :      Optional[Any] 						=     tf.constant([0.9_285, 0.9_015, -0.3_150]      )
									self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()			,     _lowerCAmelCase			,     atol=1e-4      )      )




"""simple docstring"""
import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					SpeechTaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True






							def snake_case_  (   self      ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_     :      Any 						=     SpeechTaTokenizer(a_      )

									a_     :      Optional[int] 						=     AddedToken("<mask>"			,     lstrip=a_			,     rstrip=a_      )
									a_     :      Any 						=     mask_token
									tokenizer.add_special_tokens({"mask_token": mask_token}      )
									tokenizer.add_tokens(["<ctc_blank>"]      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Tuple 						=     "this is a test"
									a_     :      Any 						=     "this is a test"
									return input_text, output_text






							def snake_case_  (   self			,     a_			,     a_=False			,     a_=2_0			,     a_=5      ):
									a_						,				a_     :      Optional[Any] 						=     self.get_input_output_texts(a_      )
									a_     :      Optional[Any] 						=     tokenizer.encode(a_			,     add_special_tokens=a_      )
									a_     :      Dict 						=     tokenizer.decode(a_			,     clean_up_tokenization_spaces=a_      )
									return text, ids






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     "<pad>"
									a_     :      Optional[int] 						=     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_      )			,     a_      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_      )			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0]			,     "<s>"      )
									self.assertEqual(vocab_keys[1]			,     "<pad>"      )
									self.assertEqual(vocab_keys[-4]			,     "œ"      )
									self.assertEqual(vocab_keys[-2]			,     "<mask>"      )
									self.assertEqual(vocab_keys[-1]			,     "<ctc_blank>"      )
									self.assertEqual(len(a_      )			,     8_1      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     7_9      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.get_tokenizers(do_lower_case=a_      )
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
													a_     :      Dict 						=     tokenizer.vocab_size
													a_     :      List[str] 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )

													# We usually have added tokens from the start in tests because our vocab fixtures are
													# smaller than the original vocabs - let's not assert this
													# self.assertEqual(vocab_size, all_size)

													a_     :      Optional[int] 						=     ["aaaaa bbbbbb", "cccccccccdddddddd"]
													a_     :      int 						=     tokenizer.add_tokens(a_      )
													a_     :      List[Any] 						=     tokenizer.vocab_size
													a_     :      Tuple 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size + len(a_      )      )

													a_     :      str 						=     tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     4      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )

													a_     :      Tuple 						=     {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
													a_     :      Dict 						=     tokenizer.add_special_tokens(a_      )
													a_     :      Optional[Any] 						=     tokenizer.vocab_size
													a_     :      Any 						=     len(a_      )

													self.assertNotEqual(a_			,     0      )
													self.assertEqual(a_			,     a_      )
													self.assertEqual(a_			,     len(a_      )      )
													self.assertEqual(a_			,     all_size_a + len(a_      )      )

													a_     :      Any 						=     tokenizer.encode(
													    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"			,     add_special_tokens=a_      )

													self.assertGreaterEqual(len(a_      )			,     6      )
													self.assertGreater(tokens[0]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[0]			,     tokens[1]      )
													self.assertGreater(tokens[-3]			,     tokenizer.vocab_size - 1      )
													self.assertGreater(tokens[-3]			,     tokens[-4]      )
													self.assertEqual(tokens[0]			,     tokenizer.eos_token_id      )
													self.assertEqual(tokens[-3]			,     tokenizer.pad_token_id      )






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									pass






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.get_tokenizer()

									a_     :      Any 						=     tokenizer.tokenize("This is a test"      )
									# fmt: off
									self.assertListEqual(a_			,     [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"]      )
									# fmt: on

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(a_      )			,     [4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6]			,     )

									a_     :      Optional[int] 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )

									a_     :      Tuple 						=     tokenizer.convert_tokens_to_ids(a_      )
									# fmt: off
									self.assertListEqual(a_			,     [4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6]      )
									# fmt: on

									a_     :      Tuple 						=     tokenizer.convert_ids_to_tokens(a_      )
									self.assertListEqual(
									    a_			,     [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]      )






							@slow
							def snake_case_  (   self      ):
									# Use custom sequence because this tokenizer does not handle numbers.
									a_     :      List[Any] 						=     [
									    "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
									    "general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
									    "Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
									    "models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
									    "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
									    "conditioning on both left and right context in all layers.",
									    "The quick brown fox jumps over the lazy dog.",
									]

									# fmt: off
									a_     :      Tuple 						=     {
									    "input_ids": [
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									        [4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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									    ],
									    "attention_mask": [
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									    ]
									}
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="microsoft/speecht5_asr"			,     revision="c5ef64c71905caeccde0e4462ef3f9077224c524"			,     sequences=a_			,     )