



import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer

class 	lowerCamelCase_    ( tf.keras.layers.Layer						):






      '''simple docstring'''







      def __init__(	self      :			int	,	__lowerCamelCase      :			Dict[str, int]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			int = None	,	__lowerCamelCase      :			int = None )      ->				str:
         super().__init__()
         A							:					Optional[Any]              =						pad_token_id
         A							:					Dict              =						max_length
         A							:					Tuple              =						vocab
         A							:					str              =						merges
         A							:					int              =						BytePairTokenizer(__lowerCamelCase	,	__lowerCamelCase	,	sequence_length=__lowerCamelCase )







      @classmethod
      def 			SCREAMING_SNAKE_CASE__		(	cls      :			Any	,	__lowerCamelCase      :			GPTaTokenizer	,	*__lowerCamelCase      :			List[str]	,	**__lowerCamelCase      :			Optional[int] )      ->				List[str]:
         A							:					Any              =						[" ".join(__lowerCamelCase ) for m in tokenizer.bpe_ranks.keys()]
         A							:					List[Any]              =						tokenizer.get_vocab()
         return cls(__lowerCamelCase	,	__lowerCamelCase	,	*__lowerCamelCase	,	**__lowerCamelCase )







      @classmethod
      def 			SCREAMING_SNAKE_CASE__		(	cls      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, os.PathLike]	,	*__lowerCamelCase      :			Optional[int]	,	**__lowerCamelCase      :			Tuple )      ->				int:
         A							:					List[Any]              =						GPTaTokenizer.from_pretrained(__lowerCamelCase	,	*__lowerCamelCase	,	**__lowerCamelCase )
         return cls.from_tokenizer(__lowerCamelCase	,	*__lowerCamelCase	,	**__lowerCamelCase )







      @classmethod
      def 			SCREAMING_SNAKE_CASE__		(	cls      :			List[Any]	,	__lowerCamelCase      :			List[Any] )      ->				Dict:
         return cls(**__lowerCamelCase )







      def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Dict:
         return {
             "vocab": self.vocab,
             "merges": self.merges,
             "max_length": self.max_length,
             "pad_token_id": self.pad_token_id,
         }







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			int = None )      ->				int:
         A							:					Optional[Any]              =						self.tf_tokenizer(__lowerCamelCase )
         A							:					int              =						tf.ones_like(__lowerCamelCase )

         if self.pad_token_id is not None:
            # pad the tokens up to max length
            A							:					Optional[int]              =						max_length if max_length is not None else self.max_length

            if max_length is not None:
               A							:					List[str]              =						pad_model_inputs(
                   __lowerCamelCase	,	max_seq_length=__lowerCamelCase	,	pad_value=self.pad_token_id )

         return {"attention_mask": attention_mask, "input_ids": input_ids}




from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			List[Any]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[Any]=3	,	__lowerCamelCase      :			Union[str, Any]=32	,	__lowerCamelCase      :			List[Any]=3	,	__lowerCamelCase      :			Optional[int]=10	,	__lowerCamelCase      :			Optional[Any]=[10, 20, 30, 40]	,	__lowerCamelCase      :			Tuple=[1, 1, 2, 1]	,	__lowerCamelCase      :			Optional[Any]=True	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			List[str]="relu"	,	__lowerCamelCase      :			Tuple=3	,	__lowerCamelCase      :			Union[str, Any]=None	,	)      ->				str:
									A							:					Optional[Any]              =						parent
									A							:					Optional[int]              =						batch_size
									A							:					List[str]              =						image_size
									A							:					List[str]              =						num_channels
									A							:					Tuple              =						embeddings_size
									A							:					Optional[int]              =						hidden_sizes
									A							:					Dict              =						depths
									A							:					Optional[int]              =						is_training
									A							:					List[str]              =						use_labels
									A							:					List[Any]              =						hidden_act
									A							:					Optional[int]              =						num_labels
									A							:					int              =						scope
									A							:					List[Any]              =						len(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				List[Any]:
									A							:					str              =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

									A							:					Optional[Any]              =						None
									if self.use_labels:
												A							:					Any              =						ids_tensor([self.batch_size]	,	self.num_labels )

									A							:					List[Any]              =						self.get_config()
									return config, pixel_values, labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									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	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Tuple:
									A							:					List[str]              =						TFRegNetModel(config=__lowerCamelCase )
									A							:					str              =						model(__lowerCamelCase	,	training=__lowerCamelCase )
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape	,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Tuple )      ->				List[str]:
									A							:					List[Any]              =						self.num_labels
									A							:					int              =						TFRegNetForImageClassification(__lowerCamelCase )
									A							:					str              =						model(__lowerCamelCase	,	labels=__lowerCamelCase	,	training=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Dict:
									A							:					Any              =						self.prepare_config_and_inputs()
									A       , A       , A							:					str              =						config_and_inputs
									A							:					Any              =						{"pixel_values": pixel_values}
									return config, inputs_dict







@require_tf
class 	lowerCamelCase_    ( _A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						a__   =  (
						    {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A							:					Optional[Any]              =						TFRegNetModelTester(self )
									A							:					int              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	has_text_modality=__lowerCamelCase )







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







						@unittest.skip(reason="RegNet does not use inputs_embeds" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[Any]:
									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."	,	)
						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Any:
									super().test_keras_fit()







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Tuple:
									A       , A							:					List[str]              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Union[str, Any]              =						model_class(__lowerCamelCase )
												A							:					int              =						inspect.signature(model.call )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												A							:					Union[str, Any]              =						[*signature.parameters.keys()]

												A							:					Optional[int]              =						["pixel_values"]
												self.assertListEqual(arg_names[:1]	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Tuple:
									A							:					Tuple              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									def check_hidden_states_output(__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			str ):
												A							:					int              =						model_class(__lowerCamelCase )
												A							:					int              =						model(**self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )	,	training=__lowerCamelCase )

												A							:					Any              =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												A							:					Dict              =						self.model_tester.num_stages
												self.assertEqual(len(__lowerCamelCase )	,	expected_num_stages + 1 )

												# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:] )	,	[self.model_tester.image_size // 2, self.model_tester.image_size // 2]	,	)

									A       , A							:					int              =						self.model_tester.prepare_config_and_inputs_for_common()
									A							:					str              =						["basic", "bottleneck"]
									for model_class in self.all_model_classes:
												for layer_type in layers_type:
															A							:					List[str]              =						layer_type
															A							:					List[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							:					Union[str, Any]              =						True

															check_hidden_states_output(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A       , A							:					Tuple              =						self.model_tester.prepare_config_and_inputs_for_common()

									def check_equivalence(__lowerCamelCase      :			int	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Optional[Any]={} ):
												A							:					Optional[int]              =						model(__lowerCamelCase	,	return_dict=__lowerCamelCase	,	**__lowerCamelCase )
												A							:					int              =						model(__lowerCamelCase	,	return_dict=__lowerCamelCase	,	**__lowerCamelCase ).to_tuple()

												def recursive_check(__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Any ):
															if isinstance(__lowerCamelCase	,	(List, Tuple) ):
																		for tuple_iterable_value, dict_iterable_value in zip(__lowerCamelCase	,	__lowerCamelCase ):
																					recursive_check(__lowerCamelCase	,	__lowerCamelCase )
															elif tuple_object is None:
																		return
															else:
																		self.assertTrue(
																		    all(tf.equal(__lowerCamelCase	,	__lowerCamelCase ) )	,	msg=(
																		        "Tuple and dict output are not equal. Difference:"
																		        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
																		    )	,	)

															recursive_check(__lowerCamelCase	,	__lowerCamelCase )

									for model_class in self.all_model_classes:
												A							:					Tuple              =						model_class(__lowerCamelCase )

												A							:					Optional[int]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Tuple              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

												A							:					Optional[int]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					List[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

												A							:					Optional[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												A							:					str              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	{"output_hidden_states": True} )

												A							:					Optional[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					Tuple              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												check_equivalence(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	{"output_hidden_states": True} )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[Any]:
									A							:					List[str]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Union[str, Any]              =						TFRegNetModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







def 	UpperCAmelCase   (						):
			A							:					Dict              =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
			return image






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






						'''simple docstring'''







						@cached_property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[Any]:
									return (
									    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
									    if is_vision_available()
									    else None
									)







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[Any]:
									A							:					List[Any]              =						TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

									A							:					Optional[int]              =						self.default_image_processor
									A							:					List[Any]              =						prepare_img()
									A							:					str              =						image_processor(images=__lowerCamelCase	,	return_tensors="tf" )

									# forward pass
									A							:					List[Any]              =						model(**__lowerCamelCase	,	training=__lowerCamelCase )

									# verify the logits
									A							:					Dict              =						tf.TensorShape((1, 10_00) )
									self.assertEqual(outputs.logits.shape	,	__lowerCamelCase )

									A							:					Optional[Any]              =						tf.constant([-0.4180, -1.5051, -3.4836] )

									tf.debugging.assert_near(outputs.logits[0, :3]	,	__lowerCamelCase	,	atol=1e-4 )