




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase_    =logging.get_logger(__name__)

UpperCAmelCase_    ={
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			List[Any]					    ="""gpt2"""
				__a				:			Union[str, Any]					    =["""past_key_values"""]
				__a				:			List[Any]					    ={
				    """hidden_size""": """n_embd""",
				    """max_position_embeddings""": """n_positions""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}



				def __init__(			self							,			UpperCAmelCase_=5_02_57							,			UpperCAmelCase_=10_24							,			UpperCAmelCase_=7_68							,			UpperCAmelCase_=12							,			UpperCAmelCase_=12							,			UpperCAmelCase_=None							,			UpperCAmelCase_="gelu_new"							,			UpperCAmelCase_=0.1							,			UpperCAmelCase_=0.1							,			UpperCAmelCase_=0.1							,			UpperCAmelCase_=1E-5							,			UpperCAmelCase_=0.02							,			UpperCAmelCase_="cls_index"							,			UpperCAmelCase_=True							,			UpperCAmelCase_=None							,			UpperCAmelCase_=True							,			UpperCAmelCase_=0.1							,			UpperCAmelCase_=True							,			UpperCAmelCase_=True							,			UpperCAmelCase_=5_02_56							,			UpperCAmelCase_=5_02_56							,			UpperCAmelCase_=False							,			UpperCAmelCase_=False							,			**UpperCAmelCase_							,			):
											lowerCAmelCase =		vocab_size
											lowerCAmelCase =		n_positions
											lowerCAmelCase =		n_embd
											lowerCAmelCase =		n_layer
											lowerCAmelCase =		n_head
											lowerCAmelCase =		n_inner
											lowerCAmelCase =		activation_function
											lowerCAmelCase =		resid_pdrop
											lowerCAmelCase =		embd_pdrop
											lowerCAmelCase =		attn_pdrop
											lowerCAmelCase =		layer_norm_epsilon
											lowerCAmelCase =		initializer_range
											lowerCAmelCase =		summary_type
											lowerCAmelCase =		summary_use_proj
											lowerCAmelCase =		summary_activation
											lowerCAmelCase =		summary_first_dropout
											lowerCAmelCase =		summary_proj_to_labels
											lowerCAmelCase =		scale_attn_weights
											lowerCAmelCase =		use_cache
											lowerCAmelCase =		scale_attn_by_inverse_layer_idx
											lowerCAmelCase =		reorder_and_upcast_attn

											lowerCAmelCase =		bos_token_id
											lowerCAmelCase =		eos_token_id

											super().__init__(bos_token_id=UpperCAmelCase_							,			eos_token_id=UpperCAmelCase_							,			**UpperCAmelCase_    )




class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_ = "default"							,			UpperCAmelCase_ = None							,			UpperCAmelCase_ = False							,			):
											super().__init__(UpperCAmelCase_							,			task=UpperCAmelCase_							,			patching_specs=UpperCAmelCase_							,			use_past=UpperCAmelCase_    )
											if not getattr(self._config							,			'''pad_token_id'''							,			UpperCAmelCase_    ):
																		# TODO: how to do that better?
																		lowerCAmelCase =		0



				@property
				def    __snake_case		(			self    ):
											lowerCAmelCase =		OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}    )
											if self.use_past:
																		self.fill_with_past_key_values_(UpperCAmelCase_							,			direction='''inputs'''    )
																		lowerCAmelCase =		{0: '''batch''', 1: '''past_sequence + sequence'''}
											else:
																		lowerCAmelCase =		{0: '''batch''', 1: '''sequence'''}

											return common_inputs



				@property
				def    __snake_case		(			self    ):
											return self._config.n_layer



				@property
				def    __snake_case		(			self    ):
											return self._config.n_head



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_ = -1							,			UpperCAmelCase_ = -1							,			UpperCAmelCase_ = False							,			UpperCAmelCase_ = None							,			):
											lowerCAmelCase =		super(UpperCAmelCase_							,			self    ).generate_dummy_inputs(
											    UpperCAmelCase_							,			batch_size=UpperCAmelCase_							,			seq_length=UpperCAmelCase_							,			is_pair=UpperCAmelCase_							,			framework=UpperCAmelCase_    )

											# We need to order the input in the way they appears in the forward()
											lowerCAmelCase =		OrderedDict({'''input_ids''': common_inputs['''input_ids''']}    )

											# Need to add the past_keys
											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''    )
																		else:
																									import torch

																									lowerCAmelCase ,  lowerCAmelCase =		common_inputs['''input_ids'''].shape
																									# Not using the same length for past_key_values
																									lowerCAmelCase =		seqlen + 2
																									lowerCAmelCase =		(
																									    batch,
																									    self.num_attention_heads,
																									    past_key_values_length,
																									    self._config.hidden_size // self.num_attention_heads,
																									)
																									lowerCAmelCase =		[
																									    (torch.zeros(UpperCAmelCase_    ), torch.zeros(UpperCAmelCase_    )) for _ in range(self.num_layers    )
																									]

											lowerCAmelCase =		common_inputs['''attention_mask''']
											if self.use_past:
																		lowerCAmelCase =		ordered_inputs['''attention_mask'''].dtype
																		lowerCAmelCase =		torch.cat(
																		    [ordered_inputs['''attention_mask'''], torch.ones(UpperCAmelCase_							,			UpperCAmelCase_							,			dtype=UpperCAmelCase_    )]							,			dim=1    )

											return ordered_inputs






				@property
				def    __snake_case		(			self    ):
											return 13









from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
		from PIL import Image

		from transformers import ConvNextImageProcessor


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=13							,			UpperCAmelCase_=32							,			UpperCAmelCase_=3							,			UpperCAmelCase_=4							,			UpperCAmelCase_=[10, 20, 30, 40]							,			UpperCAmelCase_=[2, 2, 3, 2]							,			UpperCAmelCase_=True							,			UpperCAmelCase_=True							,			UpperCAmelCase_=37							,			UpperCAmelCase_="gelu"							,			UpperCAmelCase_=10							,			UpperCAmelCase_=0.02							,			UpperCAmelCase_=3							,			UpperCAmelCase_=None							,			):
											lowerCAmelCase =		parent
											lowerCAmelCase =		batch_size
											lowerCAmelCase =		image_size
											lowerCAmelCase =		num_channels
											lowerCAmelCase =		num_stages
											lowerCAmelCase =		hidden_sizes
											lowerCAmelCase =		depths
											lowerCAmelCase =		is_training
											lowerCAmelCase =		use_labels
											lowerCAmelCase =		intermediate_size
											lowerCAmelCase =		hidden_act
											lowerCAmelCase =		type_sequence_label_size
											lowerCAmelCase =		initializer_range
											lowerCAmelCase =		scope



				def    __snake_case		(			self    ):
											lowerCAmelCase =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

											lowerCAmelCase =		self.get_config()

											return config, pixel_values, labels



				def    __snake_case		(			self    ):
											return ConvNextConfig(
											    num_channels=self.num_channels							,			hidden_sizes=self.hidden_sizes							,			depths=self.depths							,			num_stages=self.num_stages							,			hidden_act=self.hidden_act							,			is_decoder=UpperCAmelCase_							,			initializer_range=self.initializer_range							,			)



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		TFConvNextModel(config=UpperCAmelCase_    )
											lowerCAmelCase =		model(UpperCAmelCase_							,			training=UpperCAmelCase_    )
											# 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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		self.type_sequence_label_size
											lowerCAmelCase =		TFConvNextForImageClassification(UpperCAmelCase_    )
											lowerCAmelCase =		model(UpperCAmelCase_							,			labels=UpperCAmelCase_							,			training=UpperCAmelCase_    )
											self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)    )






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




@require_tf
class 				__UpperCamelCase      (			__UpperCAmelCase  ,       __UpperCAmelCase  ,       unittest.TestCase	):





				'''simple docstring'''



				__a				:			Union[str, Any]					    =(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				__a				:			Optional[int]					    =(
				    {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				__a				:			Any					    =False
				__a				:			int					    =False
				__a				:			Union[str, Any]					    =False
				__a				:			Optional[Any]					    =False
				__a				:			List[Any]					    =False



				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextModelTester(self    )
											lowerCAmelCase =		ConfigTester(
											    self							,			config_class=UpperCAmelCase_							,			has_text_modality=UpperCAmelCase_							,			hidden_size=37							,			)



				@unittest.skip(reason='''ConvNext does not use inputs_embeds'''    )
				def    __snake_case		(			self    ):
											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    __snake_case		(			self    ):
											super().test_keras_fit()



				@unittest.skip(reason='''ConvNext does not support input and output embeddings'''    )
				def    __snake_case		(			self    ):
											pass



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )
																		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]							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase_    )



				@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.'''							,			)
				def    __snake_case		(			self    ):
											super().test_dataset_conversion()



				def    __snake_case		(			self    ):
											def check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )

																		lowerCAmelCase =		model(**self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		lowerCAmelCase =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		lowerCAmelCase =		self.model_tester.num_stages
																		self.assertEqual(len(UpperCAmelCase_    )							,			expected_num_stages + 1    )

																		# ConvNext'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 // 4, self.model_tester.image_size // 4]							,			)

											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		True
																		check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

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

																		check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											def check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_={}    ):
																		lowerCAmelCase =		model(UpperCAmelCase_							,			return_dict=UpperCAmelCase_							,			**UpperCAmelCase_    )
																		lowerCAmelCase =		model(UpperCAmelCase_							,			return_dict=UpperCAmelCase_							,			**UpperCAmelCase_    ).to_tuple()

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

																									recursive_check(UpperCAmelCase_							,			UpperCAmelCase_    )

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_    )






				@slow
				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224'''    )
											self.assertIsNotNone(UpperCAmelCase_    )




def    UpperCAmelCase							(  ):
							lowerCAmelCase =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image


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





				'''simple docstring'''



				@cached_property
				def    __snake_case		(			self    ):
											return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''    ) if is_vision_available() else None






				@slow
				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''    )

											lowerCAmelCase =		self.default_image_processor
											lowerCAmelCase =		prepare_img()
											lowerCAmelCase =		image_processor(images=UpperCAmelCase_							,			return_tensors='''tf'''    )

											# forward pass
											lowerCAmelCase =		model(**UpperCAmelCase_    )

											# verify the logits
											lowerCAmelCase =		tf.TensorShape((1, 10_00)    )
											self.assertEqual(outputs.logits.shape							,			UpperCAmelCase_    )

											lowerCAmelCase =		tf.constant([-0.0260, -0.4739, 0.1911]    )

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



