
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
							import PIL

if is_torch_available():
							import torch


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







class        lowercase	(      _UpperCAmelCase	):
							lowercase_ : Union[str, Any]       =["""pixel_values"""]




							def __init__(       self     ,A__ = True     ,A__ = None     ,A__ = PILImageResampling.BILINEAR     ,A__ = True     ,A__ = 1 / 2_5_5     ,A__ = True     ,A__ = None     ,A__ = True     ,**A__     ,):
												super().__init__(**lowercase__)
												lowercase					=		size if size is not None else {"""shortest_edge""": 2_2_4}
												lowercase					=		get_size_dict(lowercase__     ,default_to_square=lowercase__)
												lowercase					=		crop_size if crop_size is not None else {"""height""": 2_5_6, """width""": 2_5_6}
												lowercase					=		get_size_dict(lowercase__     ,param_name='''crop_size''')

												lowercase					=		do_resize
												lowercase					=		size
												lowercase					=		resample
												lowercase					=		do_rescale
												lowercase					=		rescale_factor
												lowercase					=		do_center_crop
												lowercase					=		crop_size
												lowercase					=		do_flip_channel_order




							def 				A__   (       self     ,A__     ,A__     ,A__ = PIL.Image.BILINEAR     ,A__ = None     ,**A__     ,):
												lowercase					=		get_size_dict(lowercase__     ,default_to_square=lowercase__)
												if "shortest_edge" not in size:
																	raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}')
												lowercase					=		get_resize_output_image_size(lowercase__     ,size=size['''shortest_edge''']     ,default_to_square=lowercase__)
												return resize(lowercase__     ,size=lowercase__     ,resample=lowercase__     ,data_format=lowercase__     ,**lowercase__)




							def 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												lowercase					=		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 center_crop(lowercase__     ,size=(size['''height'''], size['''width'''])     ,data_format=lowercase__     ,**lowercase__)




							def 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												return rescale(lowercase__     ,scale=lowercase__     ,data_format=lowercase__     ,**lowercase__)




							def 				A__   (       self     ,A__     ,A__ = None):
												return flip_channel_order(lowercase__     ,data_format=lowercase__)




							def 				A__   (       self     ,A__     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = None     ,A__ = ChannelDimension.FIRST     ,**A__     ,):
												lowercase					=		do_resize if do_resize is not None else self.do_resize
												lowercase					=		resample if resample is not None else self.resample
												lowercase					=		do_rescale if do_rescale is not None else self.do_rescale
												lowercase					=		rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase					=		do_center_crop if do_center_crop is not None else self.do_center_crop
												lowercase					=		(
												    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
												)

												lowercase					=		size if size is not None else self.size
												lowercase					=		get_size_dict(lowercase__     ,default_to_square=lowercase__)
												lowercase					=		crop_size if crop_size is not None else self.crop_size
												lowercase					=		get_size_dict(lowercase__     ,param_name='''crop_size''')

												lowercase					=		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_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_center_crop and crop_size is None:
																	raise ValueError('''Crop size must be specified if do_center_crop is True.''')

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

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

												if do_center_crop:
																	lowercase					=		[self.center_crop(image=lowercase__     ,size=lowercase__) for image in images]

												if do_rescale:
																	lowercase					=		[self.rescale(image=lowercase__     ,scale=lowercase__) for image in images]

												# the pretrained checkpoints assume images are BGR, not RGB
												if do_flip_channel_order:
																	lowercase					=		[self.flip_channel_order(image=lowercase__) for image in images]

												lowercase					=		[to_channel_dimension_format(lowercase__     ,lowercase__) for image in images]

												lowercase					=		{"""pixel_values""": images}
												return BatchFeature(data=lowercase__     ,tensor_type=lowercase__)





							def 				A__   (       self     ,A__     ,A__ = None):
												lowercase					=		outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																	if len(lowercase__) != len(lowercase__):
																						raise ValueError(
																						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits''')

																	if is_torch_tensor(lowercase__):
																						lowercase					=		target_sizes.numpy()

																	lowercase					=		[]

																	for idx in range(len(lowercase__)):
																						lowercase					=		torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0)     ,size=target_sizes[idx]     ,mode='''bilinear'''     ,align_corners=lowercase__)
																						lowercase					=		resized_logits[0].argmax(dim=0)
																						semantic_segmentation.append(lowercase__)
												else:
																	lowercase					=		logits.argmax(dim=1)
																	lowercase					=		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

												return semantic_segmentation






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

							lowercase__	:Optional[Any]										=					"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					if rng is None:
										lowercase					=		random.Random()

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

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

					lowercase					=		np.array(lowerCAmelCase__				,    dtype=jnp.intaa	).reshape(lowerCAmelCase__	)

					return output



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					lowercase					=		ids_tensor(lowerCAmelCase__				,    vocab_size=2				,    rng=lowerCAmelCase__	)
					# make sure that at least one token is attended to for each batch
					lowercase					=		1
					return attn_mask







@require_flax
class        lowercase	:
							lowercase_ : Any       =None
							lowercase_ : List[str]       =()




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

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

												lowercase					=		jnp.ones_like(A__)
												lowercase					=		attention_mask[:max_batch_size, :sequence_length]

												# generate max 5 tokens
												lowercase					=		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()`
																	lowercase					=		config.eos_token_id
												return config, input_ids, attention_mask, max_length




							@is_pt_flax_cross_test
							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		0

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		flax_model.generate(A__).sequences
																	lowercase					=		pt_model.generate(torch.tensor(A__     ,dtype=torch.long))

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

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




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

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




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

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




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

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




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length
												lowercase					=		0.8
												lowercase					=		1_0
												lowercase					=		0.3
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

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




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

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




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

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




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

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

												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

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




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

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

												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

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





							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

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

												lowercase					=		2
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

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

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

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







@require_flax
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												lowercase					=		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''')
												lowercase					=		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')

												lowercase					=		'''Hello world'''
												lowercase					=		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'''):
																	lowercase					=		{'''foo''': '''bar'''}
																	model.generate(A__     ,**A__)




