





'''simple docstring'''

import inspect
import unittest

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

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


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       def __init__(				self  :						Dict			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Optional[Any]=3			,					lowerCAmelCase__  :						str=32			,					lowerCAmelCase__  :						Tuple=3			,					lowerCAmelCase__  :						str=10			,					lowerCAmelCase__  :						Optional[Any]=[10, 20, 30, 40]			,					lowerCAmelCase__  :						Optional[Any]=[1, 1, 2, 1]			,					lowerCAmelCase__  :						int=True			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						int="relu"			,					lowerCAmelCase__  :						str=3			,					lowerCAmelCase__  :						Dict=None			,					)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			image_size
            _UpperCamelCase							       =			num_channels
            _UpperCamelCase							       =			embeddings_size
            _UpperCamelCase							       =			hidden_sizes
            _UpperCamelCase							       =			depths
            _UpperCamelCase							       =			is_training
            _UpperCamelCase							       =			use_labels
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			num_labels
            _UpperCamelCase							       =			scope
            _UpperCamelCase							       =			len(lowerCAmelCase__	)





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

            '''simple docstring'''




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

            _UpperCamelCase							       =			self.get_config()

            return config, pixel_values





       def snake_case__		(				self  :						str	)       ->	Any:

            '''simple docstring'''




            return RegNetConfig(
                num_channels=self.num_channels			,					embeddings_size=self.embeddings_size			,					hidden_sizes=self.hidden_sizes			,					depths=self.depths			,					hidden_act=self.hidden_act			,					num_labels=self.num_labels			,					image_size=self.image_size			,					)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetModel(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)

            # Output shape (b, c, h, w)
            self.parent.assertEqual(
                result.last_hidden_state.shape			,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,					)





       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						List[str]	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.num_labels
            _UpperCamelCase							       =			FlaxRegNetForImageClassification(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)	)







       def snake_case__		(				self  :						int	)       ->	Any:

            '''simple docstring'''




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

@require_flax
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	List[str]									=       (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       _snake_case      :	Any									=       False
       _snake_case      :	Dict									=       False
       _snake_case      :	Optional[int]									=       False





       def snake_case__		(				self  :						str	)       ->	None:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetModelTester(self	)
            _UpperCamelCase							       =			ConfigTester(self			,					config_class=lowerCAmelCase__			,					has_text_modality=lowerCAmelCase__	)





       def snake_case__		(				self  :						List[Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            self.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()





       def snake_case__		(				self  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            return





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

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__	)





       @unittest.skip(reason='''RegNet does not use inputs_embeds'''	)
       def snake_case__		(				self  :						Dict	)       ->	Dict:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''RegNet does not support input and output embeddings'''	)
       def snake_case__		(				self  :						Union[str, Any]	)       ->	Dict:

            '''simple docstring'''




            pass





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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

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

                 _UpperCamelCase							       =			['''pixel_values''']
                 self.assertListEqual(arg_names[:1]			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Tuple	)       ->	Union[str, Any]:

            '''simple docstring'''




            def check_hidden_states_output(lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Optional[Any]	):
                 _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

                 _UpperCamelCase							       =			model(**self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)	)

                 _UpperCamelCase							       =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 _UpperCamelCase							       =			self.model_tester.num_stages
                 self.assertEqual(len(lowerCAmelCase__	)			,					expected_num_stages + 1	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase							       =			True
                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 _UpperCamelCase							       =			True

                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)







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

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                      _UpperCamelCase							       =			self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)
                      _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

                      @jax.jit
                      def model_jitted(lowerCAmelCase__  :						List[str]			,					**lowerCAmelCase__  :						Optional[int]	):
                           return model(pixel_values=lowerCAmelCase__			,					**lowerCAmelCase__	)

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			model_jitted(**lowerCAmelCase__	).to_tuple()

                      with self.subTest('''JIT Disabled'''	):
                           with jax.disable_jit():
                                _UpperCamelCase							       =			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	)







def    a__							(							)						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     return image




@require_flax
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @cached_property
       def snake_case__		(				self  :						Tuple	)       ->	Dict:

            '''simple docstring'''




            return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''	) if is_vision_available() else None







       @slow
       def snake_case__		(				self  :						Tuple	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''	)

            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''np'''	)

            _UpperCamelCase							       =			model(**lowerCAmelCase__	)

            # verify the logits
            _UpperCamelCase							       =			(1, 1000)
            self.assertEqual(outputs.logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			jnp.array([-0.4180, -1.5051, -3.4836]	)

            self.assertTrue(jnp.allclose(outputs.logits[0, :3]			,					lowerCAmelCase__			,					atol=1e-4	)	)










'''simple docstring'''

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	int									=       OpenAIGPTTokenizer
       _snake_case      :	Tuple									=       OpenAIGPTTokenizerFast
       _snake_case      :	Union[str, Any]									=       True
       _snake_case      :	Optional[int]									=       False





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

            '''simple docstring'''




            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _UpperCamelCase							       =			[
                '''l''',
                '''o''',
                '''w''',
                '''e''',
                '''r''',
                '''s''',
                '''t''',
                '''i''',
                '''d''',
                '''n''',
                '''w</w>''',
                '''r</w>''',
                '''t</w>''',
                '''lo''',
                '''low''',
                '''er</w>''',
                '''low</w>''',
                '''lowest</w>''',
                '''newer</w>''',
                '''wider</w>''',
                '''<unk>''',
            ]
            _UpperCamelCase							       =			dict(zip(lowerCAmelCase__			,					range(len(lowerCAmelCase__	)	)	)	)
            _UpperCamelCase							       =			['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

            _UpperCamelCase							       =			os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['''vocab_file''']	)
            _UpperCamelCase							       =			os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['''merges_file''']	)
            with open(self.vocab_file			,					'''w'''	) as fp:
                 fp.write(json.dumps(lowerCAmelCase__	)	)
            with open(self.merges_file			,					'''w'''	) as fp:
                 fp.write('''\n'''.join(lowerCAmelCase__	)	)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						List[str]	)       ->	Optional[Any]:

            '''simple docstring'''




            return "lower newer", "lower newer"





       def snake_case__		(				self  :						Tuple	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			OpenAIGPTTokenizer(self.vocab_file			,					self.merges_file	)

            _UpperCamelCase							       =			'''lower'''
            _UpperCamelCase							       =			['''low''', '''er</w>''']
            _UpperCamelCase							       =			tokenizer.tokenize(lowerCAmelCase__	)
            self.assertListEqual(lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			tokens + ['''<unk>''']
            _UpperCamelCase							       =			[14, 15, 20]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__	)			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Tuple=15	)       ->	Union[str, Any]:

            '''simple docstring'''




            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                      _UpperCamelCase							       =			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,					**lowerCAmelCase__	)

                      # Simple input
                      _UpperCamelCase							       =			'''This is a simple input'''
                      _UpperCamelCase							       =			['''This is a simple input 1''', '''This is a simple input 2''']
                      _UpperCamelCase							       =			('''This is a simple input''', '''This is a pair''')
                      _UpperCamelCase							       =			[
                          ('''This is a simple input 1''', '''This is a simple input 2'''),
                          ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                      ]

                      # Simple input tests
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Simple input
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Simple input
                      self.assertRaises(
                          lowerCAmelCase__			,					tokenizer_r.batch_encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''			,					)

                      # Pair input
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Pair input
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Pair input
                      self.assertRaises(
                          lowerCAmelCase__			,					tokenizer_r.batch_encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''			,					)







       def snake_case__		(				self  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            pass

@require_ftfy
@require_spacy
@require_tokenizers
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       pass



