
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


lowercase						:      List[str]							=							logging.get_logger(__name__)

lowercase						:      List[str]							=							{
    """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 __snake_case	(   lowerCAmelCase  ):
    _a	:		int=       "m2m_100"
    _a	:		Dict=       ["past_key_values"]
    _a	:		List[str]=       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
    def __init__(						self	,snake_case=128112	,snake_case=1024	,snake_case=12	,snake_case=4096	,snake_case=16	,snake_case=12	,snake_case=4096	,snake_case=16	,snake_case=0.05	,snake_case=0.05	,snake_case=True	,snake_case=True	,snake_case="relu"	,snake_case=1024	,snake_case=0.1	,snake_case=0.1	,snake_case=0.0	,snake_case=0.02	,snake_case=2	,snake_case=True	,snake_case=1	,snake_case=0	,snake_case=2	,**snake_case	,):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   vocab_size
           lowercase							:  Union[str, Any] 			=   max_position_embeddings
           lowercase							:  List[str] 			=   d_model
           lowercase							:  Optional[Any] 			=   encoder_ffn_dim
           lowercase							:  int 			=   encoder_layers
           lowercase							:  str 			=   encoder_attention_heads
           lowercase							:  Optional[int] 			=   decoder_ffn_dim
           lowercase							:  Any 			=   decoder_layers
           lowercase							:  Tuple 			=   decoder_attention_heads
           lowercase							:  Dict 			=   dropout
           lowercase							:  int 			=   attention_dropout
           lowercase							:  int 			=   activation_dropout
           lowercase							:  str 			=   activation_function
           lowercase							:  Any 			=   init_std
           lowercase							:  Tuple 			=   encoder_layerdrop
           lowercase							:  List[str] 			=   decoder_layerdrop
           lowercase							:  List[str] 			=   use_cache
           lowercase							:  Any 			=   encoder_layers
           lowercase							:  Dict 			=   scale_embedding  # scale factor will be sqrt(d_model) if True

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






class __snake_case	(   lowerCAmelCase  ):
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]   )

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

           if self.use_past:
                  self.fill_with_past_key_values_(snake_case	,direction="""inputs"""   )
           return common_inputs
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = -1	,snake_case = -1	,snake_case = False	,snake_case = None	,):



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   compute_effective_axis_dimension(
               snake_case	,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
           lowercase							:  Optional[int] 			=   tokenizer.num_special_tokens_to_add(snake_case   )
           lowercase							:  Optional[int] 			=   compute_effective_axis_dimension(
               snake_case	,fixed_dimension=OnnxConfig.default_fixed_sequence	,num_token_to_add=snake_case   )

           # Generate dummy inputs according to compute batch and sequence
           lowercase							:  int 			=   [""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
           lowercase							:  List[str] 			=   dict(tokenizer(snake_case	,return_tensors=snake_case   )   )
           return common_inputs
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = -1	,snake_case = -1	,snake_case = False	,snake_case = None	,):



           '''simple docstring'''







           lowercase							:  str 			=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case	,snake_case	,snake_case	,snake_case	,snake_case   )

           # Generate decoder inputs
           lowercase							:  Dict 			=   seq_length if not self.use_past else 1
           lowercase							:  Optional[int] 			=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               snake_case	,snake_case	,snake_case	,snake_case	,snake_case   )
           lowercase							:  Optional[Any] 			=   {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
           lowercase							:  List[Any] 			=   dict(**snake_case	,**snake_case   )

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

                  lowercase							:  Union[str, Any] 			=   torch.cat(
                      [common_inputs["""decoder_attention_mask"""], torch.ones(snake_case	,snake_case   )]	,dim=1   )

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

                  for _ in range(snake_case   ):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(snake_case   ),
                                 torch.zeros(snake_case   ),
                                 torch.zeros(snake_case   ),
                                 torch.zeros(snake_case   ),
                             )   )
                  # TODO: test this.
                  lowercase							:  int 			=   encoder_shape if remaining_side_name == """encoder""" else decoder_shape
                  for _ in range(snake_case	,snake_case   ):
                         common_inputs["past_key_values"].append((torch.zeros(snake_case   ), torch.zeros(snake_case   ))   )
           return common_inputs
    _a	:		str=       _generate_dummy_inputs_for_default_and_seqaseq_lm


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class __snake_case	(   unittest.TestCase  ):
    def __init__(						self	,snake_case	,snake_case=7	,snake_case=3	,snake_case=10	,snake_case=18	,snake_case=30	,snake_case=400	,snake_case=True	,snake_case=None	,snake_case=True	,snake_case=[0.5, 0.5, 0.5]	,snake_case=[0.5, 0.5, 0.5]	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   size if size is not None else {"""shortest_edge""": 18}
           lowercase							:  Tuple 			=   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           lowercase							:  Optional[int] 			=   parent
           lowercase							:  Any 			=   batch_size
           lowercase							:  int 			=   num_channels
           lowercase							:  Dict 			=   num_frames
           lowercase							:  Any 			=   image_size
           lowercase							:  str 			=   min_resolution
           lowercase							:  Any 			=   max_resolution
           lowercase							:  Tuple 			=   do_resize
           lowercase							:  Optional[Any] 			=   size
           lowercase							:  str 			=   do_normalize
           lowercase							:  Optional[Any] 			=   image_mean
           lowercase							:  Optional[Any] 			=   image_std
           lowercase							:  Union[str, Any] 			=   crop_size
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Optional[Any]=       VideoMAEImageProcessor if is_vision_available() else None
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   VideoMAEImageProcessingTester(self   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(snake_case	,"""image_mean"""   )   )
           self.assertTrue(hasattr(snake_case	,"""image_std"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_normalize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_resize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_center_crop"""   )   )
           self.assertTrue(hasattr(snake_case	,"""size"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 18}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 18, """width""": 18}   )

           lowercase							:  Any 			=   self.image_processing_class.from_dict(self.image_processor_dict	,size=42	,crop_size=84   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 42}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 84, """width""": 84}   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           pass
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           lowercase							:  Optional[int] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,Image.Image   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           lowercase							:  Optional[Any] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,numpify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,np.ndarray   )

           # Test not batched input
           lowercase							:  int 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Optional[int] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           lowercase							:  List[str] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,torchify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,torch.Tensor   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
