






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
				import os

				import jax.numpy as jnp
				from jax import jit

				from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
				from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

				a_		:						Optional[Any]             =    '0.12'  # assumed parallelism: 8


if is_torch_available():
				import torch






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=None):
			if rng is None:
						SCREAMING_SNAKE_CASE						= random.Random()

			SCREAMING_SNAKE_CASE						= 1
			for dim in shape:
						total_dims *= dim

			SCREAMING_SNAKE_CASE						= []
			for _ in range(_UpperCAmelCase):
						values.append(rng.randint(0							,       vocab_size - 1))

			SCREAMING_SNAKE_CASE						= np.array(_UpperCAmelCase							,       dtype=jnp.intaa).reshape(_UpperCAmelCase)

			return output






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=None):
			SCREAMING_SNAKE_CASE						= ids_tensor(_UpperCAmelCase							,       vocab_size=2							,       rng=_UpperCAmelCase)
			# make sure that at least one token is attended to for each batch
			SCREAMING_SNAKE_CASE						= 1
			return attn_mask

@require_flax
class 		_snake_case  :

			'''simple docstring'''





			_lowercase		:    Dict			     =							None
			_lowercase		:    Tuple			     =							()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

						# cut to half length & take max batch_size 3
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= inputs['input_ids'].shape[-1] // 2
						SCREAMING_SNAKE_CASE						= inputs['input_ids'][:max_batch_size, :sequence_length]

						SCREAMING_SNAKE_CASE						= jnp.ones_like(a)
						SCREAMING_SNAKE_CASE						= attention_mask[:max_batch_size, :sequence_length]

						# generate max 5 tokens
						SCREAMING_SNAKE_CASE						= input_ids.shape[-1] + 5
						if config.eos_token_id is not None and config.pad_token_id is None:
									# hack to allow generate for models such as GPT2 as is done in `generate()`
									SCREAMING_SNAKE_CASE						= config.eos_token_id
						return config, input_ids, attention_mask, max_length






			@is_pt_flax_cross_test
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 0

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model_class.__name__[4:]  # Skip the "Flax" at the beginning
									SCREAMING_SNAKE_CASE						= getattr(a		,					a)
									SCREAMING_SNAKE_CASE						= pt_model_class(a).eval()
									SCREAMING_SNAKE_CASE						= load_flax_weights_in_pytorch_model(a		,					flax_model.params)

									SCREAMING_SNAKE_CASE						= flax_model.generate(a).sequences
									SCREAMING_SNAKE_CASE						= pt_model.generate(torch.tensor(a		,					dtype=torch.long))

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
												SCREAMING_SNAKE_CASE						= flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

									self.assertListEqual(pt_generation_outputs.numpy().tolist()		,					flax_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					a)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(a).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					a)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(a).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 2

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					a)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(a).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= 2

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a).sequences
									self.assertEqual(generation_outputs.shape[0]		,					input_ids.shape[0] * config.num_return_sequences)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 0.8
						SCREAMING_SNAKE_CASE						= 10
						SCREAMING_SNAKE_CASE						= 0.3
						SCREAMING_SNAKE_CASE						= 1
						SCREAMING_SNAKE_CASE						= 8
						SCREAMING_SNAKE_CASE						= 9

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					a)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(a).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 1
						SCREAMING_SNAKE_CASE						= 8
						SCREAMING_SNAKE_CASE						= 9

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					a)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(a).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()
						SCREAMING_SNAKE_CASE						= max_length
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= 1
						SCREAMING_SNAKE_CASE						= 8
						SCREAMING_SNAKE_CASE						= 9

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					a)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(a).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()

						# pad attention mask on the left
						SCREAMING_SNAKE_CASE						= attention_mask.at[(0, 0)].set(0)

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a		,					attention_mask=a).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					a)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(a		,					attention_mask=a).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()

						# pad attention mask on the left
						SCREAMING_SNAKE_CASE						= attention_mask.at[(0, 0)].set(0)

						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a		,					attention_mask=a).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					a)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(a		,					attention_mask=a).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self._get_input_ids_and_config()

						# pad attention mask on the left
						SCREAMING_SNAKE_CASE						= attention_mask.at[(0, 0)].set(0)

						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= max_length

						for model_class in self.all_generative_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)

									SCREAMING_SNAKE_CASE						= model.generate(a		,					attention_mask=a).sequences
									self.assertEqual(generation_outputs.shape[-1]		,					a)

									SCREAMING_SNAKE_CASE						= jit(model.generate)
									SCREAMING_SNAKE_CASE						= jit_generate(a		,					attention_mask=a).sequences

									self.assertListEqual(generation_outputs.tolist()		,					jit_generation_outputs.tolist())



@require_flax
class 		_snake_case  (  unittest.TestCase					):

			'''simple docstring'''





			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert')
						SCREAMING_SNAKE_CASE						= FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only')

						SCREAMING_SNAKE_CASE						= 'Hello world'
						SCREAMING_SNAKE_CASE						= tokenizer(a		,					return_tensors='np').input_ids

						# typos are quickly detected (the correct argument is `do_sample`)
						with self.assertRaisesRegex(a		,					'do_samples'):
									model.generate(a		,					do_samples=a)

						# arbitrary arguments that will not be used anywhere are also not accepted
						with self.assertRaisesRegex(a		,					'foo'):
									SCREAMING_SNAKE_CASE						= {'foo': 'bar'}
									model.generate(a		,					**a)












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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


a_		:						Optional[int]             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= b.T
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=1)
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=0)
			SCREAMING_SNAKE_CASE						= np.matmul(_UpperCAmelCase							,       _UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= aa[:, None] - 2 * ab + ba[None, :]
			return d






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= x.reshape(-1							,       3)
			SCREAMING_SNAKE_CASE						= squared_euclidean_distance(_UpperCAmelCase							,       _UpperCAmelCase)
			return np.argmin(_UpperCAmelCase							,       axis=1)







class 		_snake_case  (  A__					):
			_lowercase		:    str			     =							['''pixel_values''']






			def __init__(       self		,					a = None		,					a = True		,					a = None		,					a = PILImageResampling.BILINEAR		,					a = True		,					a = True		,					**a		,					)      ->       None:
						super().__init__(**a)
						SCREAMING_SNAKE_CASE						= size if size is not None else {'height': 256, 'width': 256}
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= np.array(a) if clusters is not None else None
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= resample
						SCREAMING_SNAKE_CASE						= do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = PILImageResampling.BILINEAR		,					a = None		,					**a		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						if "height" not in size or "width" not in size:
									raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}''')
						return resize(
						    a		,					size=(size['height'], size['width'])		,					resample=a		,					data_format=a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= rescale(image=a		,					scale=1 / 1_27.5		,					data_format=a)
						SCREAMING_SNAKE_CASE						= image - 1
						return image







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = ChannelDimension.FIRST		,					**a		,					)      ->       PIL.Image.Image:
						SCREAMING_SNAKE_CASE						= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE						= size if size is not None else self.size
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE						= do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						SCREAMING_SNAKE_CASE						= clusters if clusters is not None else self.clusters
						SCREAMING_SNAKE_CASE						= np.array(a)

						SCREAMING_SNAKE_CASE						= make_list_of_images(a)

						if not valid_images(a):
									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 or resample is None:
									raise ValueError('Size and resample must be specified if do_resize is True.')

						if do_color_quantize and clusters is None:
									raise ValueError('Clusters must be specified if do_color_quantize is True.')

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE						= [to_numpy_array(a) for image in images]

						if do_resize:
									SCREAMING_SNAKE_CASE						= [self.resize(image=a		,					size=a		,					resample=a) for image in images]

						if do_normalize:
									SCREAMING_SNAKE_CASE						= [self.normalize(image=a) for image in images]

						if do_color_quantize:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					ChannelDimension.LAST) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									SCREAMING_SNAKE_CASE						= np.array(a)
									SCREAMING_SNAKE_CASE						= color_quantize(a		,					a).reshape(images.shape[:-1])

									# flatten to (batch_size, height*width)
									SCREAMING_SNAKE_CASE						= images.shape[0]
									SCREAMING_SNAKE_CASE						= images.reshape(a		,					-1)

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									SCREAMING_SNAKE_CASE						= list(a)
						else:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					a) for image in images]

						SCREAMING_SNAKE_CASE						= {'input_ids': images}
						return BatchFeature(data=a		,					tensor_type=a)




