

"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__snake_case							:     Union[str, Any] 						=	logging.get_logger(__name__)



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Union[str, Any]   			=	['''pixel_values''']





						def __init__(  self       ,     _lowercase = True       ,     _lowercase = None       ,     _lowercase = True       ,     _lowercase = None       ,     _lowercase = PILImageResampling.BICUBIC       ,     _lowercase = True       ,     _lowercase = 1 / 255       ,     _lowercase = True       ,     _lowercase = None       ,     _lowercase = None       ,     **_lowercase       ,     )      ->				None:
									super().__init__(**_lowercase  )
									a_		:		Union[str, Any]				    =				crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
									a_		:		str				    =				get_size_dict(_lowercase       ,     param_name="""crop_size"""  )
									a_		:		Any				    =				size if size is not None else {"""height""": 224, """width""": 224}
									a_		:		Tuple				    =				get_size_dict(_lowercase  )

									a_		:		List[str]				    =				do_center_crop
									a_		:		List[str]				    =				crop_size
									a_		:		Optional[Any]				    =				do_resize
									a_		:		int				    =				size
									a_		:		List[Any]				    =				resample
									a_		:		str				    =				do_rescale
									a_		:		Optional[Any]				    =				rescale_factor
									a_		:		int				    =				do_normalize
									a_		:		str				    =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									a_		:		Tuple				    =				image_std if image_std is not None else IMAGENET_DEFAULT_STD





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase = None       ,     _lowercase = None       ,     **_lowercase       ,     )      ->				np.ndarray:
									a_		:		int				    =				self.size if size is None else size
									a_		:		Optional[Any]				    =				get_size_dict(_lowercase  )
									a_		:		Any				    =				get_size_dict(_lowercase       ,     param_name="""crop_size"""  )

									a_		:		int				    =				get_image_size(_lowercase  )
									a_		:		Union[str, Any]				    =				min(_lowercase       ,     _lowercase  )
									a_		:		int				    =				(size["""height"""] / crop_size["""height"""]) * min_dim
									a_		:		str				    =				(size["""width"""] / crop_size["""width"""]) * min_dim
									return center_crop(_lowercase       ,     size=(cropped_height, cropped_width)       ,     data_format=_lowercase       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase = PIL.Image.BICUBIC       ,     _lowercase = None       ,     **_lowercase       ,     )      ->				np.ndarray:
									a_		:		Union[str, Any]				    =				get_size_dict(_lowercase  )
									if "height" not in size or "width" not in size:
												raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''  )
									return resize(
									    _lowercase       ,     size=(size["""height"""], size["""width"""])       ,     resample=_lowercase       ,     data_format=_lowercase       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase = None       ,     **_lowercase       ,     )      ->				Dict:
									return rescale(_lowercase       ,     scale=_lowercase       ,     data_format=_lowercase       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase = None       ,     **_lowercase       ,     )      ->				np.ndarray:
									return normalize(_lowercase       ,     mean=_lowercase       ,     std=_lowercase       ,     data_format=_lowercase       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = ChannelDimension.FIRST       ,     **_lowercase       ,     )      ->				PIL.Image.Image:
									a_		:		List[str]				    =				do_center_crop if do_center_crop is not None else self.do_center_crop
									a_		:		Optional[Any]				    =				crop_size if crop_size is not None else self.crop_size
									a_		:		str				    =				get_size_dict(_lowercase       ,     param_name="""crop_size"""  )
									a_		:		Dict				    =				do_resize if do_resize is not None else self.do_resize
									a_		:		Optional[Any]				    =				size if size is not None else self.size
									a_		:		int				    =				get_size_dict(_lowercase  )
									a_		:		Optional[int]				    =				resample if resample is not None else self.resample
									a_		:		Any				    =				do_rescale if do_rescale is not None else self.do_rescale
									a_		:		Optional[int]				    =				rescale_factor if rescale_factor is not None else self.rescale_factor
									a_		:		Optional[int]				    =				do_normalize if do_normalize is not None else self.do_normalize
									a_		:		str				    =				image_mean if image_mean is not None else self.image_mean
									a_		:		List[Any]				    =				image_std if image_std is not None else self.image_std

									a_		:		Dict				    =				make_list_of_images(_lowercase  )

									if not valid_images(_lowercase  ):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

									if do_center_crop and crop_size is None:
												raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""  )

									if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""  )

									if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

									if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and image standard deviation must be specified if do_normalize is True."""  )

									# All transformations expect numpy arrays.
									a_		:		str				    =				[to_numpy_array(_lowercase  ) for image in images]

									if do_center_crop:
												a_		:		Any				    =				[self.center_crop(_lowercase       ,     _lowercase       ,     size=_lowercase  ) for image in images]

									if do_resize:
												a_		:		Optional[Any]				    =				[self.resize(image=_lowercase       ,     size=_lowercase       ,     resample=_lowercase  ) for image in images]

									if do_rescale:
												a_		:		Optional[int]				    =				[self.rescale(image=_lowercase       ,     scale=_lowercase  ) for image in images]

									if do_normalize:
												a_		:		Optional[Any]				    =				[self.normalize(image=_lowercase       ,     mean=_lowercase       ,     std=_lowercase  ) for image in images]

									a_		:		Tuple				    =				[to_channel_dimension_format(_lowercase       ,     _lowercase  ) for image in images]

									a_		:		Any				    =				{"""pixel_values""": images}
									return BatchFeature(data=_lowercase       ,     tensor_type=_lowercase  )





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


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

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch



class    A__:
						"""simple docstring"""






						def __init__(  self       ,     _lowercase       ,     _lowercase=14       ,     _lowercase=7       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=True       ,     _lowercase=99       ,     _lowercase=32       ,     _lowercase=5       ,     _lowercase=4       ,     _lowercase=37       ,     _lowercase="gelu"       ,     _lowercase=0.1       ,     _lowercase=0.1       ,     _lowercase=512       ,     _lowercase=0.0_2       ,     )      ->				Dict:
									a_		:		List[str]				    =				parent
									a_		:		Tuple				    =				batch_size
									a_		:		int				    =				seq_length
									a_		:		Tuple				    =				is_training
									a_		:		List[Any]				    =				use_input_mask
									a_		:		Dict				    =				use_token_type_ids
									a_		:		List[Any]				    =				use_labels
									a_		:		Optional[int]				    =				vocab_size
									a_		:		Any				    =				hidden_size
									a_		:		List[Any]				    =				num_hidden_layers
									a_		:		List[str]				    =				num_attention_heads
									a_		:		List[str]				    =				intermediate_size
									a_		:		List[str]				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		Tuple				    =				attention_probs_dropout_prob
									a_		:		Union[str, Any]				    =				max_position_embeddings
									a_		:		Tuple				    =				initializer_range
									a_		:		Any				    =				None
									a_		:		str				    =				vocab_size - 1
									a_		:		Optional[Any]				    =				vocab_size - 1
									a_		:		List[Any]				    =				vocab_size - 1





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		Dict				    =				ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size  )

									a_		:		List[str]				    =				None
									if self.use_input_mask:
												a_		:		Dict				    =				random_attention_mask([self.batch_size, self.seq_length]  )

									a_		:		Optional[int]				    =				GPTaConfig(
									    vocab_size=self.vocab_size       ,     n_embd=self.hidden_size       ,     n_layer=self.num_hidden_layers       ,     n_head=self.num_attention_heads       ,     n_positions=self.max_position_embeddings       ,     use_cache=_lowercase       ,     bos_token_id=self.bos_token_id       ,     eos_token_id=self.eos_token_id       ,     pad_token_id=self.pad_token_id       ,     )

									return (config, input_ids, input_mask)





						def 	UpperCamelCase__			(  self  )      ->				int:
									a_		:		Any				    =				self.prepare_config_and_inputs()
									a_    ,			a_    ,			a_		:		List[str]				    =				config_and_inputs
									a_		:		Optional[Any]				    =				{"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_    ,			a_    ,			a_		:		Dict				    =				self.prepare_config_and_inputs()

									a_		:		List[str]				    =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
									a_		:		Optional[int]				    =				ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2  )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				Optional[Any]:
									a_		:		Dict				    =				20
									a_		:		Tuple				    =				model_class_name(_lowercase  )

									a_		:		Tuple				    =				model.init_cache(input_ids.shape[0]       ,     _lowercase  )
									a_		:		Any				    =				jnp.ones((input_ids.shape[0], max_decoder_length)       ,     dtype="""i4"""  )

									a_		:		str				    =				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)  )
									a_		:		int				    =				model(
									    input_ids[:, :-1]       ,     attention_mask=_lowercase       ,     past_key_values=_lowercase       ,     position_ids=_lowercase       ,     )

									a_		:		int				    =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype="""i4"""  )
									a_		:		Dict				    =				model(
									    input_ids[:, -1:]       ,     attention_mask=_lowercase       ,     past_key_values=outputs_cache.past_key_values       ,     position_ids=_lowercase       ,     )

									a_		:		Dict				    =				model(_lowercase  )

									a_		:		Tuple				    =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1e-3       ,     msg=F'''Max diff is {diff}'''  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				int:
									a_		:		Any				    =				20
									a_		:		Optional[int]				    =				model_class_name(_lowercase  )

									a_		:		Any				    =				jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]       ,     axis=-1       ,     )

									a_		:		Optional[Any]				    =				model.init_cache(input_ids.shape[0]       ,     _lowercase  )
									a_		:		Any				    =				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1  )[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)  )

									a_		:		Optional[Any]				    =				model(
									    input_ids[:, :-1]       ,     attention_mask=_lowercase       ,     past_key_values=_lowercase       ,     position_ids=_lowercase       ,     )
									a_		:		List[str]				    =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype="""i4"""  )
									a_		:		Dict				    =				model(
									    input_ids[:, -1:]       ,     past_key_values=outputs_cache.past_key_values       ,     attention_mask=_lowercase       ,     position_ids=_lowercase       ,     )

									a_		:		str				    =				model(_lowercase       ,     attention_mask=_lowercase  )

									a_		:		List[str]				    =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1e-3       ,     msg=F'''Max diff is {diff}'''  )







@require_flax
class    A__(a_,       a_,       unittest.TestCase    ):
						"""simple docstring"""






						_A  :							List[str]   			=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
						_A  :							Dict   			=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		List[str]				    =				FlaxGPTaModelTester(self  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									for model_class_name in self.all_model_classes:
												a_    ,			a_    ,			a_		:		Any				    =				self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(_lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									for model_class_name in self.all_model_classes:
												a_    ,			a_    ,			a_		:		Optional[Any]				    =				self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )





						@slow
						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		str				    =				GPTaTokenizer.from_pretrained("""gpt2"""       ,     pad_token="""</s>"""       ,     padding_side="""left"""  )
									a_		:		Optional[int]				    =				tokenizer(["""Hello this is a long string""", """Hey"""]       ,     return_tensors="""np"""       ,     padding=_lowercase       ,     truncation=_lowercase  )

									a_		:		Any				    =				FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""  )
									a_		:		List[Any]				    =				False
									a_		:		List[Any]				    =				model.config.eos_token_id

									a_		:		Tuple				    =				jax.jit(model.generate  )

									a_		:		Union[str, Any]				    =				jit_generate(inputs["""input_ids"""]       ,     attention_mask=inputs["""attention_mask"""]  ).sequences

									a_		:		Dict				    =				tokenizer.batch_decode(_lowercase       ,     skip_special_tokens=_lowercase  )

									a_		:		Tuple				    =				[
									    """Hello this is a long string of words. I'm going to start with the first one.\n""",
									    """Hey, I'm not sure if I'm going to be able to do""",
									]

									self.assertListEqual(_lowercase       ,     _lowercase  )





						@is_pt_flax_cross_test
						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									a_    ,			a_		:		Optional[int]				    =				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
															# prepare inputs
															a_		:		List[Any]				    =				self._prepare_for_class(_lowercase       ,     _lowercase  )
															a_		:		List[str]				    =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															a_		:		List[str]				    =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a_		:		List[str]				    =				getattr(_lowercase       ,     _lowercase  )

															a_    ,			a_		:		Any				    =				pt_inputs["""input_ids"""].shape
															a_		:		Any				    =				np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)  )
															for batch_idx, start_index in enumerate(_lowercase  ):
																		a_		:		Dict				    =				0
																		a_		:		int				    =				1
																		a_		:		int				    =				0
																		a_		:		Optional[Any]				    =				1
															a_		:		Optional[int]				    =				pt_model_class(_lowercase  ).eval()
															a_		:		int				    =				model_class(_lowercase       ,     dtype=jnp.floataa  )

															a_		:		Optional[int]				    =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,     _lowercase  )
															a_		:		Union[str, Any]				    =				fx_state

															with torch.no_grad():
																		a_		:		str				    =				pt_model(**_lowercase  ).to_tuple()

															a_		:		List[str]				    =				fx_model(**_lowercase  ).to_tuple()
															self.assertEqual(len(_lowercase  )       ,     len(_lowercase  )       ,     """Output lengths differ between Flax and PyTorch"""  )
															for fx_output, pt_output in zip(_lowercase       ,     _lowercase  ):
																		self.assert_almost_equals(fx_output[:, -1]       ,     pt_output[:, -1].numpy()       ,     4e-2  )

															with tempfile.TemporaryDirectory() as tmpdirname:
																		pt_model.save_pretrained(_lowercase  )
																		a_		:		Dict				    =				model_class.from_pretrained(_lowercase       ,     from_pt=_lowercase  )

															a_		:		int				    =				fx_model_loaded(**_lowercase  ).to_tuple()
															self.assertEqual(
															    len(_lowercase  )       ,     len(_lowercase  )       ,     """Output lengths differ between Flax and PyTorch"""  )
															for fx_output_loaded, pt_output in zip(_lowercase       ,     _lowercase  ):
																		self.assert_almost_equals(fx_output_loaded[:, -1]       ,     pt_output[:, -1].numpy()       ,     4e-2  )





						@is_pt_flax_cross_test
						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_    ,			a_		:		str				    =				self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
															# prepare inputs
															a_		:		int				    =				self._prepare_for_class(_lowercase       ,     _lowercase  )
															a_		:		Optional[Any]				    =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															a_		:		List[str]				    =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
															a_		:		Union[str, Any]				    =				getattr(_lowercase       ,     _lowercase  )

															a_		:		Tuple				    =				pt_model_class(_lowercase  ).eval()
															a_		:		Dict				    =				model_class(_lowercase       ,     dtype=jnp.floataa  )

															a_		:		Optional[Any]				    =				load_flax_weights_in_pytorch_model(_lowercase       ,     fx_model.params  )
															a_    ,			a_		:		Dict				    =				pt_inputs["""input_ids"""].shape
															a_		:		Any				    =				np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)  )
															for batch_idx, start_index in enumerate(_lowercase  ):
																		a_		:		Optional[int]				    =				0
																		a_		:		Dict				    =				1
																		a_		:		Union[str, Any]				    =				0
																		a_		:		Tuple				    =				1

															# make sure weights are tied in PyTorch
															pt_model.tie_weights()

															with torch.no_grad():
																		a_		:		str				    =				pt_model(**_lowercase  ).to_tuple()

															a_		:		str				    =				fx_model(**_lowercase  ).to_tuple()
															self.assertEqual(len(_lowercase  )       ,     len(_lowercase  )       ,     """Output lengths differ between Flax and PyTorch"""  )
															for fx_output, pt_output in zip(_lowercase       ,     _lowercase  ):
																		self.assert_almost_equals(fx_output[:, -1]       ,     pt_output[:, -1].numpy()       ,     4e-2  )

															with tempfile.TemporaryDirectory() as tmpdirname:
																		fx_model.save_pretrained(_lowercase  )
																		a_		:		Dict				    =				pt_model_class.from_pretrained(_lowercase       ,     from_flax=_lowercase  )

															with torch.no_grad():
																		a_		:		List[str]				    =				pt_model_loaded(**_lowercase  ).to_tuple()

															self.assertEqual(
															    len(_lowercase  )       ,     len(_lowercase  )       ,     """Output lengths differ between Flax and PyTorch"""  )
															for fx_output, pt_output in zip(_lowercase       ,     _lowercase  ):
																		self.assert_almost_equals(fx_output[:, -1]       ,     pt_output[:, -1].numpy()       ,     4e-2  )





						@slow
						def 	UpperCamelCase__			(  self  )      ->				Any:
									for model_class_name in self.all_model_classes:
												a_		:		List[str]				    =				model_class_name.from_pretrained("""gpt2"""       ,     from_pt=_lowercase  )
												a_		:		List[str]				    =				model(np.ones((1, 1)  )  )
												self.assertIsNotNone(_lowercase  )


