

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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


_lowerCamelCase							:  Dict     =				logging.get_logger(__name__)





class    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	["""pixel_values"""]



   def __init__(				self				:	int,						__A				:	bool = True,						__A				:	Dict[str, int] = None,						__A				:	PILImageResampling = PIL.Image.NEAREST,						__A				:	bool = False,						__A				:	Dict[str, int] = None,						__A				:	Union[int, float] = 1 / 2_5_5,						__A				:	bool = False,						__A				:	bool = True,						__A				:	bool = True,						__A				:	Optional[Union[float, List[float]]] = None,						__A				:	Optional[Union[float, List[float]]] = None,						__A				:	bool = True,						**__A				:	str,						):
     super().__init__(**__A		)
     UpperCAmelCase			:				Any										=					size if size is not None else {'''height''': 3_4_6, '''width''': 3_4_6}
     UpperCAmelCase			:				str										=					get_size_dict(__A		)
     UpperCAmelCase			:				Tuple										=					crop_size if crop_size is not None else {'''height''': 2_8_9, '''width''': 2_8_9}
     UpperCAmelCase			:				str										=					get_size_dict(__A,						param_name='''crop_size'''		)

     UpperCAmelCase			:				Optional[Any]										=					do_resize
     UpperCAmelCase			:				Dict										=					size
     UpperCAmelCase			:				List[Any]										=					resample
     UpperCAmelCase			:				Any										=					do_center_crop
     UpperCAmelCase			:				str										=					crop_size
     UpperCAmelCase			:				Optional[int]										=					do_rescale
     UpperCAmelCase			:				List[str]										=					rescale_factor
     UpperCAmelCase			:				int										=					rescale_offset
     UpperCAmelCase			:				Dict										=					do_normalize
     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
     UpperCAmelCase			:				Dict										=					include_top



   def 			__magic_name__       (				self				:	List[Any],						__A				:	np.ndarray,						__A				:	Dict[str, int],						__A				:	PILImageResampling = PIL.Image.NEAREST,						__A				:	Optional[Union[str, ChannelDimension]] = None,						**__A				:	Optional[Any],						):
     UpperCAmelCase			:				Optional[int]										=					get_size_dict(__A		)
     if "height" not in size or "width" not in size:
       raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
     return resize(
         __A,						size=(size['''height'''], size['''width''']),						resample=__A,						data_format=__A,						**__A		)



   def 			__magic_name__       (				self				:	Optional[int],						__A				:	np.ndarray,						__A				:	Dict[str, int],						__A				:	Optional[Union[str, ChannelDimension]] = None,						**__A				:	Optional[int],						):
     UpperCAmelCase			:				Optional[Any]										=					get_size_dict(__A		)
     if "height" not in size or "width" not in size:
       raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
     return center_crop(__A,						size=(size['''height'''], size['''width''']),						data_format=__A,						**__A		)



   def 			__magic_name__       (				self				:	Union[str, Any],						__A				:	np.ndarray,						__A				:	Union[int, float],						__A				:	bool = True,						__A				:	Optional[Union[str, ChannelDimension]] = None,						**__A				:	int,						):
     if offset:
       UpperCAmelCase			:				Union[str, Any]										=					(image - 1_2_7.5) * scale
       if data_format is not None:
         UpperCAmelCase			:				Any										=					to_channel_dimension_format(__A,						__A		)
       UpperCAmelCase			:				Optional[int]										=					rescaled_image.astype(np.floataa		)
     else:
       UpperCAmelCase			:				Optional[int]										=					rescale(__A,						scale=__A,						data_format=__A,						**__A		)
     return rescaled_image



   def 			__magic_name__       (				self				:	Optional[Any],						__A				:	np.ndarray,						__A				:	Union[float, List[float]],						__A				:	Union[float, List[float]],						__A				:	Optional[Union[str, ChannelDimension]] = None,						**__A				:	List[str],						):
     return normalize(__A,						mean=__A,						std=__A,						data_format=__A,						**__A		)





   def 			__magic_name__       (				self				:	Any,						__A				:	ImageInput,						__A				:	bool = None,						__A				:	Dict[str, int] = None,						__A				:	List[Any]=None,						__A				:	bool = None,						__A				:	Dict[str, int] = None,						__A				:	bool = None,						__A				:	float = None,						__A				:	bool = None,						__A				:	bool = None,						__A				:	Optional[Union[float, List[float]]] = None,						__A				:	Optional[Union[float, List[float]]] = None,						__A				:	bool = None,						__A				:	Optional[Union[str, TensorType]] = None,						__A				:	ChannelDimension = ChannelDimension.FIRST,						**__A				:	Optional[int],						):
     UpperCAmelCase			:				List[Any]										=					do_resize if do_resize is not None else self.do_resize
     UpperCAmelCase			:				int										=					resample if resample is not None else self.resample
     UpperCAmelCase			:				Any										=					do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCAmelCase			:				List[str]										=					do_rescale if do_rescale is not None else self.do_rescale
     UpperCAmelCase			:				Union[str, Any]										=					rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCAmelCase			:				List[Any]										=					rescale_offset if rescale_offset is not None else self.rescale_offset
     UpperCAmelCase			:				Any										=					do_normalize if do_normalize is not None else self.do_normalize
     UpperCAmelCase			:				int										=					image_mean if image_mean is not None else self.image_mean
     UpperCAmelCase			:				int										=					image_std if image_std is not None else self.image_std
     UpperCAmelCase			:				str										=					include_top if include_top is not None else self.include_top

     UpperCAmelCase			:				Any										=					size if size is not None else self.size
     UpperCAmelCase			:				Dict										=					get_size_dict(__A		)
     UpperCAmelCase			:				Dict										=					crop_size if crop_size is not None else self.crop_size
     UpperCAmelCase			:				Any										=					get_size_dict(__A,						param_name='''crop_size'''		)

     UpperCAmelCase			:				Optional[Any]										=					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_center_crop and crop_size is None:
       raise ValueError('''Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.'''		)

     # All transformations expect numpy arrays.
     UpperCAmelCase			:				List[Any]										=					[to_numpy_array(__A		) for image in images]

     if do_resize:
       UpperCAmelCase			:				Union[str, Any]										=					[self.resize(image=__A,						size=__A,						resample=__A		) for image in images]

     if do_center_crop:
       UpperCAmelCase			:				str										=					[self.center_crop(image=__A,						size=__A		) for image in images]

     if do_rescale:
       UpperCAmelCase			:				str										=					[self.rescale(image=__A,						scale=__A,						offset=__A		) for image in images]

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

     if include_top:
       UpperCAmelCase			:				int										=					[self.normalize(image=__A,						mean=[0, 0, 0],						std=__A		) for image in images]

     UpperCAmelCase			:				Dict										=					[to_channel_dimension_format(__A,						__A		) for image in images]

     UpperCAmelCase			:				Dict										=					{'''pixel_values''': images}
     return BatchFeature(data=__A,						tensor_type=__A		)








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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


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

_lowerCamelCase							:  Optional[int]     =				{
    "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	"""m2m_100"""
   UpperCamelCase  	=	["""past_key_values"""]
   UpperCamelCase  	=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



   def __init__(				self				:	int,						__A				:	Optional[Any]=1_2_8_1_1_2,						__A				:	str=1_0_2_4,						__A				:	Tuple=1_2,						__A				:	Any=4_0_9_6,						__A				:	str=1_6,						__A				:	List[str]=1_2,						__A				:	int=4_0_9_6,						__A				:	List[Any]=1_6,						__A				:	Tuple=0.0_5,						__A				:	Tuple=0.0_5,						__A				:	List[Any]=True,						__A				:	int=True,						__A				:	str="relu",						__A				:	str=1_0_2_4,						__A				:	str=0.1,						__A				:	str=0.1,						__A				:	List[str]=0.0,						__A				:	List[str]=0.0_2,						__A				:	Any=2,						__A				:	List[str]=True,						__A				:	Tuple=1,						__A				:	Optional[Any]=0,						__A				:	Optional[Any]=2,						**__A				:	Optional[Any],						):
     UpperCAmelCase			:				str										=					vocab_size
     UpperCAmelCase			:				Optional[Any]										=					max_position_embeddings
     UpperCAmelCase			:				List[str]										=					d_model
     UpperCAmelCase			:				Any										=					encoder_ffn_dim
     UpperCAmelCase			:				Optional[int]										=					encoder_layers
     UpperCAmelCase			:				List[Any]										=					encoder_attention_heads
     UpperCAmelCase			:				str										=					decoder_ffn_dim
     UpperCAmelCase			:				Any										=					decoder_layers
     UpperCAmelCase			:				List[str]										=					decoder_attention_heads
     UpperCAmelCase			:				str										=					dropout
     UpperCAmelCase			:				int										=					attention_dropout
     UpperCAmelCase			:				Dict										=					activation_dropout
     UpperCAmelCase			:				Tuple										=					activation_function
     UpperCAmelCase			:				Any										=					init_std
     UpperCAmelCase			:				Union[str, Any]										=					encoder_layerdrop
     UpperCAmelCase			:				List[Any]										=					decoder_layerdrop
     UpperCAmelCase			:				Optional[Any]										=					use_cache
     UpperCAmelCase			:				str										=					encoder_layers
     UpperCAmelCase			:				List[Any]										=					scale_embedding  # scale factor will be sqrt(d_model) if True

     super().__init__(
         pad_token_id=__A,						bos_token_id=__A,						eos_token_id=__A,						is_encoder_decoder=__A,						decoder_start_token_id=__A,						**__A,						)






class    __UpperCAmelCase	(			lowerCamelCase__		):



   @property
   def 			__magic_name__       (				self				:	List[str]		):
     UpperCAmelCase			:				List[Any]										=					OrderedDict(
         [
             ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
         ]		)

     if self.use_past:
       UpperCAmelCase			:				List[str]										=					{0: '''batch'''}
       UpperCAmelCase			:				Any										=					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
     else:
       UpperCAmelCase			:				Any										=					{0: '''batch''', 1: '''decoder_sequence'''}
       UpperCAmelCase			:				List[Any]										=					{0: '''batch''', 1: '''decoder_sequence'''}

     if self.use_past:
       self.fill_with_past_key_values_(__A,						direction='''inputs'''		)
     return common_inputs



   def 			__magic_name__       (				self				:	Dict,						__A				:	PreTrainedTokenizer,						__A				:	int = -1,						__A				:	int = -1,						__A				:	bool = False,						__A				:	Optional[TensorType] = None,						):
     # Copied from OnnxConfig.generate_dummy_inputs
     # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     UpperCAmelCase			:				List[str]										=					compute_effective_axis_dimension(
         __A,						fixed_dimension=OnnxConfig.default_fixed_batch,						num_token_to_add=0		)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     UpperCAmelCase			:				Optional[int]										=					tokenizer.num_special_tokens_to_add(__A		)
     UpperCAmelCase			:				int										=					compute_effective_axis_dimension(
         __A,						fixed_dimension=OnnxConfig.default_fixed_sequence,						num_token_to_add=__A		)

     # Generate dummy inputs according to compute batch and sequence
     UpperCAmelCase			:				List[str]										=					[''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
     UpperCAmelCase			:				Tuple										=					dict(tokenizer(__A,						return_tensors=__A		)		)
     return common_inputs



   def 			__magic_name__       (				self				:	Any,						__A				:	PreTrainedTokenizer,						__A				:	int = -1,						__A				:	int = -1,						__A				:	bool = False,						__A				:	Optional[TensorType] = None,						):
     UpperCAmelCase			:				int										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A,						__A,						__A,						__A,						__A		)

     # Generate decoder inputs
     UpperCAmelCase			:				Union[str, Any]										=					seq_length if not self.use_past else 1
     UpperCAmelCase			:				Optional[int]										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A,						__A,						__A,						__A,						__A		)
     UpperCAmelCase			:				Dict										=					{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
     UpperCAmelCase			:				Optional[int]										=					dict(**__A,						**__A		)

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
       else:
         import torch
       UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					common_inputs['''input_ids'''].shape
       UpperCAmelCase			:				Dict										=					common_inputs['''decoder_input_ids'''].shape[1]
       UpperCAmelCase					,		UpperCAmelCase			:				Dict										=					self.num_attention_heads
       UpperCAmelCase			:				Union[str, Any]										=					(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       UpperCAmelCase			:				str										=					decoder_seq_length + 3
       UpperCAmelCase			:				Union[str, Any]										=					(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       UpperCAmelCase			:				Union[str, Any]										=					torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(__A,						__A		)],						dim=1		)

       UpperCAmelCase			:				Any										=					[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       UpperCAmelCase					,		UpperCAmelCase			:				str										=					self.num_layers
       UpperCAmelCase			:				Any										=					min(__A,						__A		)
       UpperCAmelCase			:				Dict										=					max(__A,						__A		) - min_num_layers
       UpperCAmelCase			:				Tuple										=					'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

       for _ in range(__A		):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(__A		),
                 torch.zeros(__A		),
                 torch.zeros(__A		),
                 torch.zeros(__A		),
             )		)
       # TODO: test this.
       UpperCAmelCase			:				List[str]										=					encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
       for _ in range(__A,						__A		):
         common_inputs["past_key_values"].append((torch.zeros(__A		), torch.zeros(__A		))		)
     return common_inputs





   UpperCamelCase  	=	_generate_dummy_inputs_for_default_and_seqaseq_lm





