






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						'▁'

__UpperCAmelCase								=						{'vocab_file': 'sentencepiece.bpe.model'}

__UpperCAmelCase								=						{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__UpperCAmelCase								=						{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
__UpperCAmelCase								=						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  VOCAB_FILES_NAMES
		_snake_case    :    str       						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case    :    str       						=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case    :    Union[str, Any]       						=  ['''input_ids''', '''attention_mask''']

		_snake_case    :    List[int]       						=  []
		_snake_case    :    List[int]       						=  []



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase="</s>"				,		_UpperCamelCase="</s>"				,		_UpperCamelCase="<s>"				,		_UpperCamelCase="<unk>"				,		_UpperCamelCase="<pad>"				,		_UpperCamelCase="<mask>"				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							None:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_    :      List[str]            =						AddedToken(_UpperCamelCase				,		lstrip=_UpperCamelCase				,		rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) else mask_token

							UpperCAmelCase_    :      Union[str, Any]            =						{} if sp_model_kwargs is None else sp_model_kwargs

							UpperCAmelCase_    :      str            =						kwargs.get('additional_special_tokens'				,		[]   )
							kwargs["additional_special_tokens"] += [
							    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=_UpperCamelCase				,		tgt_lang=_UpperCamelCase				,		eos_token=_UpperCamelCase				,		unk_token=_UpperCamelCase				,		sep_token=_UpperCamelCase				,		cls_token=_UpperCamelCase				,		pad_token=_UpperCamelCase				,		mask_token=_UpperCamelCase				,		sp_model_kwargs=self.sp_model_kwargs				,		**_UpperCamelCase				,		)

							UpperCAmelCase_    :      Dict            =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(_UpperCamelCase   )   )
							UpperCAmelCase_    :      str            =						vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							UpperCAmelCase_    :      Optional[int]            =						{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							UpperCAmelCase_    :      Union[str, Any]            =						1

							UpperCAmelCase_    :      Union[str, Any]            =						len(self.sp_model   )
							UpperCAmelCase_    :      Optional[Any]            =						{
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCamelCase   )
							}
							UpperCAmelCase_    :      int            =						{v: k for k, v in self.lang_code_to_id.items()}
							UpperCAmelCase_    :      Optional[int]            =						len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

							self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
							UpperCAmelCase_    :      Optional[Any]            =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}

							UpperCAmelCase_    :      Optional[int]            =						src_lang if src_lang is not None else 'en_XX'
							UpperCAmelCase_    :      Tuple            =						self.lang_code_to_id[self._src_lang]
							UpperCAmelCase_    :      Any            =						tgt_lang
							self.set_src_lang_special_tokens(self._src_lang   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							return len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset + 1  # Plus 1 for the mask token



		@property
		def 					__UpperCAmelCase			(							self   )	->							str:
							return self._src_lang



		@src_lang.setter
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							None:
							UpperCAmelCase_    :      Optional[Any]            =						new_src_lang
							self.set_src_lang_special_tokens(self._src_lang   )



		def __getstate__(							self   )	->							Dict:
							UpperCAmelCase_    :      List[str]            =						self.__dict__.copy()
							UpperCAmelCase_    :      str            =						None
							return state



		def __setstate__(							self				,		_UpperCamelCase   )	->							None:
							UpperCAmelCase_    :      Optional[int]            =						d

							# for backward compatibility
							if not hasattr(self				,		'sp_model_kwargs'   ):
												UpperCAmelCase_    :      List[str]            =						{}

							UpperCAmelCase_    :      Optional[int]            =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(self.vocab_file   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      List[str]            =						{self.convert_ids_to_tokens(_UpperCamelCase   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[str]:
							return self.sp_model.encode(_UpperCamelCase				,		out_type=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							int:
							if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
							UpperCAmelCase_    :      int            =						self.sp_model.PieceToId(_UpperCamelCase   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							str:
							if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Union[str, Any]            =						[]
							UpperCAmelCase_    :      List[Any]            =						''
							UpperCAmelCase_    :      Optional[Any]            =						False
							for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																	if not prev_is_special:
																						out_string += " "
																	out_string += self.sp_model.decode(_UpperCamelCase   ) + token
																	UpperCAmelCase_    :      Optional[Any]            =						True
																	UpperCAmelCase_    :      Union[str, Any]            =						[]
												else:
																	current_sub_tokens.append(_UpperCamelCase   )
																	UpperCAmelCase_    :      Optional[int]            =						False
							out_string += self.sp_model.decode(_UpperCamelCase   )
							return out_string.strip()



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							Tuple[str]:
							if not os.path.isdir(_UpperCamelCase   ):
												logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
												return
							UpperCAmelCase_    :      Tuple            =						os.path.join(
							    _UpperCamelCase				,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(_UpperCamelCase   ) and os.path.isfile(self.vocab_file   ):
												copyfile(self.vocab_file				,		_UpperCamelCase   )
							elif not os.path.isfile(self.vocab_file   ):
												with open(_UpperCamelCase				,		'wb'   ) as fi:
																	UpperCAmelCase_    :      Tuple            =						self.sp_model.serialized_model_proto()
																	fi.write(_UpperCamelCase   )

							return (out_vocab_file,)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = False   )	->							List[int]:

							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=_UpperCamelCase				,		token_ids_a=_UpperCamelCase				,		already_has_special_tokens=_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						[1] * len(self.prefix_tokens   )
							UpperCAmelCase_    :      Optional[int]            =						[1] * len(self.suffix_tokens   )
							if token_ids_a is None:
												return prefix_ones + ([0] * len(_UpperCamelCase   )) + suffix_ones
							return prefix_ones + ([0] * len(_UpperCamelCase   )) + ([0] * len(_UpperCamelCase   )) + suffix_ones



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							List[int]:
							if token_ids_a is None:
												return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							if src_lang is None or tgt_lang is None:
												raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
							UpperCAmelCase_    :      str            =						src_lang
							UpperCAmelCase_    :      Any            =						self(_UpperCamelCase				,		add_special_tokens=_UpperCamelCase				,		return_tensors=_UpperCamelCase				,		**_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						self.convert_tokens_to_ids(_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						tgt_lang_id
							return inputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = "en_XX"				,		_UpperCamelCase = None				,		_UpperCamelCase = "ro_RO"				,		**_UpperCamelCase				,		)	->							BatchEncoding:
							UpperCAmelCase_    :      Optional[int]            =						src_lang
							UpperCAmelCase_    :      Optional[Any]            =						tgt_lang
							return super().prepare_seqaseq_batch(_UpperCamelCase				,		_UpperCamelCase				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							return self.set_src_lang_special_tokens(self.src_lang   )



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							return self.set_tgt_lang_special_tokens(self.tgt_lang   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							None:
							UpperCAmelCase_    :      str            =						self.lang_code_to_id[src_lang]
							UpperCAmelCase_    :      Union[str, Any]            =						[self.cur_lang_code_id]
							UpperCAmelCase_    :      Union[str, Any]            =						[self.eos_token_id]



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							None:
							UpperCAmelCase_    :      int            =						self.lang_code_to_id[tgt_lang]
							UpperCAmelCase_    :      str            =						[self.cur_lang_code_id]
							UpperCAmelCase_    :      List[str]            =						[self.eos_token_id]









"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

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


if is_flax_available():
   import jax

   from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor




class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def __init__(			self							:			str,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			int=1_0_0,					UpperCAmelCase__							:			Any=1_3,					UpperCAmelCase__							:			List[Any]=3_0,					UpperCAmelCase__							:			Dict=2,					UpperCAmelCase__							:			Any=3,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			List[str]=True,					UpperCAmelCase__							:			Optional[Any]=3_2,					UpperCAmelCase__							:			Any=5,					UpperCAmelCase__							:			Any=4,					UpperCAmelCase__							:			Any=3_7,					UpperCAmelCase__							:			Optional[int]="gelu",					UpperCAmelCase__							:			Dict=0.1,					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Dict=1_0,					UpperCAmelCase__							:			Tuple=0.02,					UpperCAmelCase__							:			List[Any]=3,					):
    __lowercase								=						parent
    __lowercase								=						vocab_size
    __lowercase								=						batch_size
    __lowercase								=						image_size
    __lowercase								=						patch_size
    __lowercase								=						num_channels
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range

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


 def 					_lowercase (			self							:			int	):
    __lowercase								=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

    __lowercase								=						None
    if self.use_labels:
       __lowercase								=						ids_tensor([self.batch_size],					self.type_sequence_label_size	)

    __lowercase								=						BeitConfig(
        vocab_size=self.vocab_size,					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, labels


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						FlaxBeitModel(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length - 1, self.vocab_size)	)


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any]	):
    __lowercase								=						self.type_sequence_label_size
    __lowercase								=						FlaxBeitForImageClassification(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)	)

    # test greyscale images
    __lowercase								=						1
    __lowercase								=						FlaxBeitForImageClassification(UpperCAmelCase__	)

    __lowercase								=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
    __lowercase								=						model(UpperCAmelCase__	)



 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs
    __lowercase								=						{"pixel_values": pixel_values}
    return config, inputs_dict







@require_flax
class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						FlaxBeitModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					has_text_modality=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowercase								=						["pixel_values"]
       self.assertListEqual(arg_names[:1],					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__	):
          __lowercase								=						self._prepare_for_class(UpperCAmelCase__,					UpperCAmelCase__	)
          __lowercase								=						model_class(UpperCAmelCase__	)

          @jax.jit
          def model_jitted(UpperCAmelCase__							:			str,					**UpperCAmelCase__							:			Dict	):
             return model(pixel_values=UpperCAmelCase__,					**UpperCAmelCase__	)

          with self.subTest("JIT Enabled"	):
             __lowercase								=						model_jitted(**UpperCAmelCase__	).to_tuple()

          with self.subTest("JIT Disabled"	):
             with jax.disable_jit():
                __lowercase								=						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	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			Union[str, Any]	):
    for model_class_name in self.all_model_classes:
       __lowercase								=						model_class_name.from_pretrained("microsoft/beit-base-patch16-224"	)
       __lowercase								=						model(np.ones((1, 3, 2_2_4, 2_2_4)	)	)
       self.assertIsNotNone(UpperCAmelCase__	)







def   _A  (  )	->						str:
   '''simple docstring'''


   __lowercase								=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
   return image




@require_vision
@require_flax
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @cached_property
 def 					_lowercase (			self							:			Optional[int]	):
    return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"	) if is_vision_available() else None


 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	).pixel_values

    # prepare bool_masked_pos
    __lowercase								=						np.ones((1, 1_9_6),					dtype=UpperCAmelCase__	)

    # forward pass
    __lowercase								=						model(pixel_values=UpperCAmelCase__,					bool_masked_pos=UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_9_6, 8_1_9_2)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array(
        [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]	)

    self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3],					UpperCAmelCase__,					atol=1E-2	)	)


 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_0_0_0)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array([-1.2_385, -1.0_987, -1.0_108]	)

    self.assertTrue(np.allclose(logits[0, :3],					UpperCAmelCase__,					atol=1E-4	)	)

    __lowercase								=						2_8_1
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 2_1_8_4_1)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						np.array([1.6_881, -0.2_787, 0.5_901]	)

    self.assertTrue(np.allclose(logits[0, :3],					UpperCAmelCase__,					atol=1E-4	)	)

    __lowercase								=						2_3_9_6
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)




