
"""simple docstring"""

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        _lowercase   :




	def __init__( self		, UpperCAmelCase_		, UpperCAmelCase_=14		, UpperCAmelCase_=7		, UpperCAmelCase_=True		, UpperCAmelCase_=True		, UpperCAmelCase_=False		, UpperCAmelCase_=True		, UpperCAmelCase_=99		, UpperCAmelCase_=32		, UpperCAmelCase_=5		, UpperCAmelCase_=4		, UpperCAmelCase_=37		, UpperCAmelCase_="gelu"		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.1		, UpperCAmelCase_=512		, UpperCAmelCase_=0.02		, )		->							Union[str, Any]:
			lowerCamelCase     :			Optional[int]		=    parent
			lowerCamelCase     :			Tuple		=    batch_size
			lowerCamelCase     :			Any		=    seq_length
			lowerCamelCase     :			int		=    is_training
			lowerCamelCase     :			Optional[Any]		=    use_input_mask
			lowerCamelCase     :			int		=    use_token_type_ids
			lowerCamelCase     :			Any		=    use_labels
			lowerCamelCase     :			Union[str, Any]		=    vocab_size
			lowerCamelCase     :			int		=    hidden_size
			lowerCamelCase     :			List[str]		=    num_hidden_layers
			lowerCamelCase     :			List[str]		=    num_attention_heads
			lowerCamelCase     :			Union[str, Any]		=    intermediate_size
			lowerCamelCase     :			List[str]		=    hidden_act
			lowerCamelCase     :			List[Any]		=    hidden_dropout_prob
			lowerCamelCase     :			str		=    attention_probs_dropout_prob
			lowerCamelCase     :			Union[str, Any]		=    max_position_embeddings
			lowerCamelCase     :			Tuple		=    initializer_range
			lowerCamelCase     :			List[str]		=    None
			lowerCamelCase     :			Optional[int]		=    vocab_size - 1
			lowerCamelCase     :			Union[str, Any]		=    vocab_size - 1
			lowerCamelCase     :			Optional[Any]		=    vocab_size - 1




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			Tuple		=    ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size					)

			lowerCamelCase     :			Dict		=    None
			if self.use_input_mask:
					lowerCamelCase     :			int		=    random_attention_mask([self.batch_size, self.seq_length]					)

			lowerCamelCase     :			Dict		=    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=snake_case__		, 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:
			lowerCamelCase     :			int		=    self.prepare_config_and_inputs()
			lowerCamelCase     :			List[str]		=    config_and_inputs
			lowerCamelCase     :			Tuple		=    {"input_ids": input_ids, "attention_mask": attention_mask}
			return config, inputs_dict




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			str		=    self.prepare_config_and_inputs()

			lowerCamelCase     :			Optional[Any]		=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
			lowerCamelCase     :			Any		=    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		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Any:
			lowerCamelCase     :			Dict		=    20
			lowerCamelCase     :			Dict		=    model_class_name(snake_case__					)

			lowerCamelCase     :			str		=    model.init_cache(input_ids.shape[0]		, snake_case__					)
			lowerCamelCase     :			List[Any]		=    jnp.ones((input_ids.shape[0], max_decoder_length)		, dtype='i4'					)

			lowerCamelCase     :			Union[str, Any]		=    jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1					)[None, :]		, (input_ids.shape[0], input_ids.shape[-1] - 1)					)
			lowerCamelCase     :			str		=    model(
			    input_ids[:, :-1]		, attention_mask=snake_case__		, past_key_values=snake_case__		, position_ids=snake_case__		, )

			lowerCamelCase     :			str		=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		, dtype='i4'					)
			lowerCamelCase     :			List[Any]		=    model(
			    input_ids[:, -1:]		, attention_mask=snake_case__		, past_key_values=outputs_cache.past_key_values		, position_ids=snake_case__		, )

			lowerCamelCase     :			List[Any]		=    model(snake_case__					)

			lowerCamelCase     :			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		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Tuple:
			lowerCamelCase     :			str		=    20
			lowerCamelCase     :			List[str]		=    model_class_name(snake_case__					)

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

			lowerCamelCase     :			Any		=    model.init_cache(input_ids.shape[0]		, snake_case__					)
			lowerCamelCase     :			int		=    jnp.broadcast_to(
			    jnp.arange(input_ids.shape[-1] - 1					)[None, :]		, (input_ids.shape[0], input_ids.shape[-1] - 1)					)

			lowerCamelCase     :			List[Any]		=    model(
			    input_ids[:, :-1]		, attention_mask=snake_case__		, past_key_values=snake_case__		, position_ids=snake_case__		, )
			lowerCamelCase     :			Tuple		=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		, dtype='i4'					)
			lowerCamelCase     :			List[str]		=    model(
			    input_ids[:, -1:]		, past_key_values=outputs_cache.past_key_values		, attention_mask=snake_case__		, position_ids=snake_case__		, )

			lowerCamelCase     :			Optional[int]		=    model(snake_case__		, attention_mask=snake_case__					)

			lowerCamelCase     :			Optional[Any]		=    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        _lowercase   (				__UpperCAmelCase				,       __UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
	lowercase_        =	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			int		=    FlaxGPTaModelTester(self					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			for model_class_name in self.all_model_classes:
					lowerCamelCase     :			Optional[int]		=    self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward(snake_case__		, snake_case__		, snake_case__		, snake_case__					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			for model_class_name in self.all_model_classes:
					lowerCamelCase     :			List[str]		=    self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward_with_attn_mask(
					    snake_case__		, snake_case__		, snake_case__		, snake_case__					)




	@slow
	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Dict		=    GPTaTokenizer.from_pretrained('gpt2'		, pad_token='</s>'		, padding_side='left'					)
			lowerCamelCase     :			Optional[int]		=    tokenizer(['Hello this is a long string', 'Hey']		, return_tensors='np'		, padding=snake_case__		, truncation=snake_case__					)

			lowerCamelCase     :			Optional[Any]		=    FlaxGPTaLMHeadModel.from_pretrained('gpt2'					)
			lowerCamelCase     :			List[Any]		=    False
			lowerCamelCase     :			Dict		=    model.config.eos_token_id

			lowerCamelCase     :			str		=    jax.jit(model.generate					)

			lowerCamelCase     :			Optional[int]		=    jit_generate(inputs['input_ids']		, attention_mask=inputs['attention_mask']					).sequences

			lowerCamelCase     :			Optional[Any]		=    tokenizer.batch_decode(snake_case__		, skip_special_tokens=snake_case__					)

			lowerCamelCase     :			str		=    [
			    "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(snake_case__		, snake_case__					)




	@is_pt_flax_cross_test
	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			List[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
							lowerCamelCase     :			Optional[Any]		=    self._prepare_for_class(snake_case__		, snake_case__					)
							lowerCamelCase     :			Dict		=    {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

							# load corresponding PyTorch class
							lowerCamelCase     :			str		=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
							lowerCamelCase     :			Optional[Any]		=    getattr(snake_case__		, snake_case__					)

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

							lowerCamelCase     :			int		=    convert_pytorch_state_dict_to_flax(pt_model.state_dict()		, snake_case__					)
							lowerCamelCase     :			List[str]		=    fx_state

							with torch.no_grad():
									lowerCamelCase     :			str		=    pt_model(**snake_case__					).to_tuple()

							lowerCamelCase     :			Optional[int]		=    fx_model(**snake_case__					).to_tuple()
							self.assertEqual(len(snake_case__					)		, len(snake_case__					)		, 'Output lengths differ between Flax and PyTorch'					)
							for fx_output, pt_output in zip(snake_case__		, snake_case__					):
									self.assert_almost_equals(fx_output[:, -1]		, pt_output[:, -1].numpy()		, 4E-2					)

							with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(snake_case__					)
									lowerCamelCase     :			List[Any]		=    model_class.from_pretrained(snake_case__		, from_pt=snake_case__					)

							lowerCamelCase     :			Dict		=    fx_model_loaded(**snake_case__					).to_tuple()
							self.assertEqual(
							    len(snake_case__					)		, len(snake_case__					)		, 'Output lengths differ between Flax and PyTorch'					)
							for fx_output_loaded, pt_output in zip(snake_case__		, snake_case__					):
									self.assert_almost_equals(fx_output_loaded[:, -1]		, pt_output[:, -1].numpy()		, 4E-2					)




	@is_pt_flax_cross_test
	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			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
							lowerCamelCase     :			Optional[Any]		=    self._prepare_for_class(snake_case__		, snake_case__					)
							lowerCamelCase     :			Tuple		=    {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

							lowerCamelCase     :			Dict		=    pt_model_class(snake_case__					).eval()
							lowerCamelCase     :			Union[str, Any]		=    model_class(snake_case__		, dtype=jnp.floataa					)

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

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

							with torch.no_grad():
									lowerCamelCase     :			Dict		=    pt_model(**snake_case__					).to_tuple()

							lowerCamelCase     :			Optional[int]		=    fx_model(**snake_case__					).to_tuple()
							self.assertEqual(len(snake_case__					)		, len(snake_case__					)		, 'Output lengths differ between Flax and PyTorch'					)
							for fx_output, pt_output in zip(snake_case__		, snake_case__					):
									self.assert_almost_equals(fx_output[:, -1]		, pt_output[:, -1].numpy()		, 4E-2					)

							with tempfile.TemporaryDirectory() as tmpdirname:
									fx_model.save_pretrained(snake_case__					)
									lowerCamelCase     :			List[str]		=    pt_model_class.from_pretrained(snake_case__		, from_flax=snake_case__					)

							with torch.no_grad():
									lowerCamelCase     :			Dict		=    pt_model_loaded(**snake_case__					).to_tuple()

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




	@slow
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for model_class_name in self.all_model_classes:
					lowerCamelCase     :			Any		=    model_class_name.from_pretrained('gpt2'		, from_pt=snake_case__					)
					lowerCamelCase     :			Any		=    model(np.ones((1, 1)					)					)
					self.assertIsNotNone(snake_case__					)














"""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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCAmelCase     :     Any            =	logging.get_logger(__name__)




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =['''pixel_values''']
   def __init__(						self	:     str				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Dict[str, int]] = None				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     bool = True				,       snake_case__	:     Union[int, float] = 1 / 2_5_5				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        super().__init__(**snake_case__				)
        UpperCAmelCase__		:  Dict				=				size if size is not None else {"height": 2_2_4, "width": 2_2_4}
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)
        UpperCAmelCase__		:  Optional[Any]				=				do_resize
        UpperCAmelCase__		:  Any				=				do_rescale
        UpperCAmelCase__		:  Optional[int]				=				do_normalize
        UpperCAmelCase__		:  Dict				=				size
        UpperCAmelCase__		:  Optional[Any]				=				resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor
        UpperCAmelCase__		:  str				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCAmelCase__		:  Dict				=				image_std if image_std is not None else IMAGENET_STANDARD_STD
   def 	__a				(						self	:     Optional[Any]				,       snake_case__	:     np.ndarray				,       snake_case__	:     Dict[str, int]				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				get_size_dict(snake_case__				)
        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(
            snake_case__				,       size=(size["height"], size["width"])				,       resample=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     Any				,       snake_case__	:     np.ndarray				,       snake_case__	:     float				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Any				):


        '''simple docstring'''
        return rescale(snake_case__				,       scale=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     str				,       snake_case__	:     np.ndarray				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     List[Any]				,       ):


        '''simple docstring'''
        return normalize(snake_case__				,       mean=snake_case__				,       std=snake_case__				,       data_format=snake_case__				,       **snake_case__				)




   def 	__a				(						self	:     Union[str, Any]				,       snake_case__	:     ImageInput				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Dict[str, int] = None				,       snake_case__	:     PILImageResampling = None				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[float] = None				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[str, TensorType]] = None				,       snake_case__	:     Union[str, ChannelDimension] = ChannelDimension.FIRST				,       **snake_case__	:     List[Any]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Union[str, Any]				=				do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase__		:  Optional[Any]				=				do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase__		:  str				=				do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase__		:  Dict				=				resample if resample is not None else self.resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase__		:  Optional[int]				=				image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase__		:  Optional[int]				=				image_std if image_std is not None else self.image_std

        UpperCAmelCase__		:  Any				=				size if size is not None else self.size
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)

        UpperCAmelCase__		:  List[Any]				=				make_list_of_images(snake_case__				)

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

        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."				)

        # All transformations expect numpy arrays.
        UpperCAmelCase__		:  Optional[int]				=				[to_numpy_array(snake_case__				) for image in images]

        if do_resize:
             UpperCAmelCase__		:  List[Any]				=				[self.resize(image=snake_case__				,       size=snake_case__				,       resample=snake_case__				) for image in images]

        if do_rescale:
             UpperCAmelCase__		:  Tuple				=				[self.rescale(image=snake_case__				,       scale=snake_case__				) for image in images]

        if do_normalize:
             UpperCAmelCase__		:  List[Any]				=				[self.normalize(image=snake_case__				,       mean=snake_case__				,       std=snake_case__				) for image in images]

        UpperCAmelCase__		:  Any				=				[to_channel_dimension_format(snake_case__				,       snake_case__				) for image in images]

        UpperCAmelCase__		:  str				=				{"pixel_values": images}
        return BatchFeature(data=snake_case__				,       tensor_type=snake_case__				)


