





import inspect
import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
			import jax

			from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=1_3       ,  __lowerCAmelCase=3_0       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=3_7       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=1_0       ,  __lowerCAmelCase=0.02       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		image_size
				lowerCamelCase__								=		patch_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		is_training
				lowerCamelCase__								=		use_labels
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		type_sequence_label_size
				lowerCamelCase__								=		initializer_range

				# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				lowerCamelCase__								=		(image_size // patch_size) ** 2
				lowerCamelCase__								=		num_patches + 1





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				lowerCamelCase__								=		ViTConfig(
				    image_size=self.image_size       ,  patch_size=self.patch_size       ,  num_channels=self.num_channels       ,  hidden_size=self.hidden_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       ,  is_decoder=__lowerCAmelCase       ,  initializer_range=self.initializer_range       ,  )

				return config, pixel_values





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		FlaxViTModel(config=__lowerCAmelCase							)
				lowerCamelCase__								=		model(__lowerCAmelCase							)
				# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
				lowerCamelCase__								=		(self.image_size, self.image_size)
				lowerCamelCase__								=		(self.patch_size, self.patch_size)
				lowerCamelCase__								=		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
				self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, num_patches + 1, self.hidden_size)							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.type_sequence_label_size
				lowerCamelCase__								=		FlaxViTForImageClassification(config=__lowerCAmelCase							)
				lowerCamelCase__								=		model(__lowerCAmelCase							)
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)							)

				# test greyscale images
				lowerCamelCase__								=		1
				lowerCamelCase__								=		FlaxViTForImageClassification(__lowerCAmelCase							)

				lowerCamelCase__								=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				lowerCamelCase__								=		model(__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.prepare_config_and_inputs()
				(
				    (
				    lowerCamelCase__		
				)				,	(
				    lowerCamelCase__		
				)				,			
				)								=		config_and_inputs
				lowerCamelCase__								=		{'''pixel_values''': pixel_values}
				return config, inputs_dict






@require_flax
class 					__A    (    snake_case_			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		FlaxViTModelTester(self							)
				lowerCamelCase__								=		ConfigTester(self       ,  config_class=__lowerCAmelCase       ,  has_text_modality=__lowerCAmelCase       ,  hidden_size=3_7							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					lowerCamelCase__								=		model_class(__lowerCAmelCase							)
					lowerCamelCase__								=		inspect.signature(model.__call__							)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCamelCase__								=		[*signature.parameters.keys()]

					lowerCamelCase__								=		['''pixel_values''']
					self.assertListEqual(arg_names[:1]       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__							):
						lowerCamelCase__								=		self._prepare_for_class(__lowerCAmelCase       ,  __lowerCAmelCase							)
						lowerCamelCase__								=		model_class(__lowerCAmelCase							)

						@jax.jit
						def model_jitted(__lowerCAmelCase       ,  **__lowerCAmelCase							):
							return model(pixel_values=__lowerCAmelCase       ,  **__lowerCAmelCase							)

						with self.subTest('''JIT Enabled'''							):
							lowerCamelCase__								=		model_jitted(**__lowerCAmelCase							).to_tuple()

						with self.subTest('''JIT Disabled'''							):
							with jax.disable_jit():
								lowerCamelCase__								=		model_jitted(**__lowerCAmelCase							).to_tuple()

						self.assertEqual(len(__lowerCAmelCase							)       ,  len(__lowerCAmelCase							)							)
						for jitted_output, output in zip(__lowerCAmelCase       ,  __lowerCAmelCase							):
							self.assertEqual(jitted_output.shape       ,  output.shape							)





			@slow
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for model_class_name in self.all_model_classes:
					lowerCamelCase__								=		model_class_name.from_pretrained('''google/vit-base-patch16-224'''							)
					lowerCamelCase__								=		model(np.ones((1, 3, 2_2_4, 2_2_4)							)							)
					self.assertIsNotNone(__lowerCAmelCase							)











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase  							=					{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

lowercase  							=					{
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


lowercase  							=					{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= SqueezeBertTokenizer




	def __init__(       self       ,			a=None       ,			a=None       ,			a=True       ,			a="[UNK]"       ,			a="[SEP]"       ,			a="[PAD]"       ,			a="[CLS]"       ,			a="[MASK]"       ,			a=True       ,			a=None       ,			**a       ,			)       ->    Tuple:
								super().__init__(
								    a       ,			tokenizer_file=a       ,			do_lower_case=a       ,			unk_token=a       ,			sep_token=a       ,			pad_token=a       ,			cls_token=a       ,			mask_token=a       ,			tokenize_chinese_chars=a       ,			strip_accents=a       ,			**a       ,			)

								snake_case_				=							json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
								if (
								    normalizer_state.get('lowercase'       ,			a					) != do_lower_case
								    or normalizer_state.get('strip_accents'       ,			a					) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'       ,			a					) != tokenize_chinese_chars
								):
															snake_case_				=							getattr(a       ,			normalizer_state.pop('type'					)					)
															snake_case_				=							do_lower_case
															snake_case_				=							strip_accents
															snake_case_				=							tokenize_chinese_chars
															snake_case_				=							normalizer_class(**a					)

								snake_case_				=							do_lower_case




	def 							_UpperCamelCase			(       self       ,			a       ,			a=None					)       ->    Tuple:
								snake_case_				=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

								return output




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[self.cls_token_id]
								if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								snake_case_				=							self._tokenizer.model.save(a       ,			name=a					)
								return tuple(a					)





