

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  lowercase				(       unittest.TestCase			):




				def __init__(					self      ,   snake_case      ,   snake_case=13      ,   snake_case=30      ,   snake_case=2      ,   snake_case=3      ,   snake_case=True      ,   snake_case=True      ,   snake_case=32      ,   snake_case=5      ,   snake_case=4      ,   snake_case=37      ,   snake_case="gelu"      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=10      ,   snake_case=0.02      ,   ):
											snake_case_							=    parent
											snake_case_							=    batch_size
											snake_case_							=    image_size
											snake_case_							=    patch_size
											snake_case_							=    num_channels
											snake_case_							=    is_training
											snake_case_							=    use_labels
											snake_case_							=    hidden_size
											snake_case_							=    num_hidden_layers
											snake_case_							=    num_attention_heads
											snake_case_							=    intermediate_size
											snake_case_							=    hidden_act
											snake_case_							=    hidden_dropout_prob
											snake_case_							=    attention_probs_dropout_prob
											snake_case_							=    type_sequence_label_size
											snake_case_							=    initializer_range

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




				def     a							(					self       ):
											snake_case_							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

											snake_case_							=    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=UpperCAmelCase_      ,   initializer_range=self.initializer_range      ,   )

											return config, pixel_values




				def     a							(					self      ,   snake_case      ,   snake_case       ):
											snake_case_							=    FlaxViTModel(config=UpperCAmelCase_       )
											snake_case_							=    model(UpperCAmelCase_       )
											# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
											snake_case_							=    (self.image_size, self.image_size)
											snake_case_							=    (self.patch_size, self.patch_size)
											snake_case_							=    (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     a							(					self      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.type_sequence_label_size
											snake_case_							=    FlaxViTForImageClassification(config=UpperCAmelCase_       )
											snake_case_							=    model(UpperCAmelCase_       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)       )

											# test greyscale images
											snake_case_							=    1
											snake_case_							=    FlaxViTForImageClassification(UpperCAmelCase_       )

											snake_case_							=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											snake_case_							=    model(UpperCAmelCase_       )




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







@require_flax
class  lowercase				(       _UpperCAmelCase	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




				def     a							(					self       ):
											snake_case_							=    FlaxViTModelTester(self       )
											snake_case_							=    ConfigTester(self      ,   config_class=UpperCAmelCase_      ,   has_text_modality=UpperCAmelCase_      ,   hidden_size=37       )




				def     a							(					self       ):
											self.config_tester.run_common_tests()




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase_       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_       )




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

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

																		snake_case_							=    ['pixel_values']
																		self.assertListEqual(arg_names[:1]      ,   UpperCAmelCase_       )




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__       ):
																									snake_case_							=    self._prepare_for_class(UpperCAmelCase_      ,   UpperCAmelCase_       )
																									snake_case_							=    model_class(UpperCAmelCase_       )

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

																									with self.subTest('JIT Enabled'       ):
																																snake_case_							=    model_jitted(**UpperCAmelCase_       ).to_tuple()

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

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




				@slow
				def     a							(					self       ):
											for model_class_name in self.all_model_classes:
																		snake_case_							=    model_class_name.from_pretrained('google/vit-base-patch16-224'       )
																		snake_case_							=    model(np.ones((1, 3, 224, 224)       )       )
																		self.assertIsNotNone(UpperCAmelCase_       )






import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {"""vocab_file""": """vocab.txt"""}

__snake_case								=  {
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

__snake_case								=  {
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}






def 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					with open(UpperCamelCase_	,	'r'		) as f:
										SCREAMING_SNAKE_CASE__													= f.read().splitlines()
										return [l.strip() for l in lines]




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Tuple				       =VOCAB_FILES_NAMES
	A__    :   Union[str, Any]				       =PRETRAINED_VOCAB_FILES_MAP
	A__    :   Any				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :   Any				       =["""input_ids""", """attention_mask"""]
	def __init__(			self							:   Optional[int]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Tuple="<unk>"       ,   UpperCAmelCase_							:   Optional[Any]="<cls>"       ,   UpperCAmelCase_							:   List[Any]="<pad>"       ,   UpperCAmelCase_							:   Optional[int]="<mask>"       ,   UpperCAmelCase_							:   Optional[int]="<eos>"       ,   **UpperCAmelCase_							:   Optional[int]       ,   ):
						super().__init__(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= load_vocab_file(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= dict(enumerate(self.all_tokens							)							)
						SCREAMING_SNAKE_CASE__													= {tok: ind for ind, tok in enumerate(self.all_tokens							)}
						SCREAMING_SNAKE_CASE__													= unk_token
						SCREAMING_SNAKE_CASE__													= cls_token
						SCREAMING_SNAKE_CASE__													= pad_token
						SCREAMING_SNAKE_CASE__													= mask_token
						SCREAMING_SNAKE_CASE__													= eos_token
						SCREAMING_SNAKE_CASE__													= self.all_tokens
						self._create_trie(self.unique_no_split_tokens							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Tuple       ,   **UpperCAmelCase_							:   List[Any]							):
						return text.split()
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   Optional[Any]=False							):
						return len(self._id_to_token							)
	def      A_				(			self							:   Union[str, Any]							):
						return {token: i for i, token in enumerate(self.all_tokens							)}
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]
						SCREAMING_SNAKE_CASE__													= [self.eos_token_id]  # No sep token in ESM vocabulary
						if token_ids_a is None:
											if self.eos_token_id is None:
																return cls + token_ids_a
											else:
																return cls + token_ids_a + sep
						elif self.eos_token_id is None:
											raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'							)
						return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   List       ,   UpperCAmelCase_							:   Optional[List] = None       ,   UpperCAmelCase_							:   bool = False							):
						if already_has_special_tokens:
											if token_ids_a is not None:
																raise ValueError(
																    'You should not supply a second sequence if the provided sequence of '
																    'ids is already formatted with special tokens for the model.'							)

											return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
						SCREAMING_SNAKE_CASE__													= [1] + ([0] * len(UpperCAmelCase_							)) + [1]
						if token_ids_a is not None:
											mask += [0] * len(UpperCAmelCase_							) + [1]
						return mask
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Any							):
						SCREAMING_SNAKE_CASE__													= os.path.join(UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'							)
						with open(UpperCAmelCase_       ,   'w'							) as f:
											f.write('\n'.join(self.all_tokens							)							)
						return (vocab_file,)
	@property
	def      A_				(			self							:   int							):
						return self.get_vocab_size(with_added_tokens=UpperCAmelCase_							)





	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Union[List[str], List[AddedToken]]       ,   UpperCAmelCase_							:   bool = False							):
						return super()._add_tokens(UpperCAmelCase_       ,   special_tokens=UpperCAmelCase_							)





