




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_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import OwlViTImageProcessor

class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def __init__(  self       :			str			,							snake_case       :			Any			,							snake_case       :			Union[str, Any]=7			,							snake_case       :			Optional[Any]=3			,							snake_case       :			str=18			,							snake_case       :			Union[str, Any]=30			,							snake_case       :			List[Any]=400			,							snake_case       :			List[Any]=True			,							snake_case       :			int=None			,							snake_case       :			Union[str, Any]=True			,							snake_case       :			str=None			,							snake_case       :			Any=True			,							snake_case       :			Optional[Any]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,							snake_case       :			Optional[int]=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,							snake_case       :			List[Any]=True			,							):
          __UpperCamelCase			=    parent
          __UpperCamelCase			=    batch_size
          __UpperCamelCase			=    num_channels
          __UpperCamelCase			=    image_size
          __UpperCamelCase			=    min_resolution
          __UpperCamelCase			=    max_resolution
          __UpperCamelCase			=    do_resize
          __UpperCamelCase			=    size if size is not None else {'''height''': 18, '''width''': 18}
          __UpperCamelCase			=    do_center_crop
          __UpperCamelCase			=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          __UpperCamelCase			=    do_normalize
          __UpperCamelCase			=    image_mean
          __UpperCamelCase			=    image_std
          __UpperCamelCase			=    do_convert_rgb







      def    snake_case						(  self       :			Union[str, Any]					):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_convert_rgb": self.do_convert_rgb,
          }







@require_torch
@require_vision
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			List[str]     =       OwlViTImageProcessor if is_vision_available() else None







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    OwlViTImageProcessingTester(self					)







      @property
      def    snake_case						(  self       :			Optional[int]					):
          return self.image_processor_tester.prepare_image_processor_dict()







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          self.assertTrue(hasattr(snake_case			,							'''do_resize'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''size'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_center_crop'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''center_crop'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_normalize'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''image_mean'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''image_std'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_convert_rgb'''					)					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    self.image_processing_class.from_dict(self.image_processor_dict					)
          self.assertEqual(image_processor.size			,							{'''height''': 18, '''width''': 18}					)
          self.assertEqual(image_processor.crop_size			,							{'''height''': 18, '''width''': 18}					)

          __UpperCamelCase			=    self.image_processing_class.from_dict(self.image_processor_dict			,							size=42			,							crop_size=84					)
          self.assertEqual(image_processor.size			,							{'''height''': 42, '''width''': 42}					)
          self.assertEqual(image_processor.crop_size			,							{'''height''': 84, '''width''': 84}					)







      def    snake_case						(  self       :			Tuple					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random PIL images
          __UpperCamelCase			=    prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case					)

          for image in image_inputs:
              self.assertIsInstance(snake_case			,							Image.Image					)

          # Test not batched input
          __UpperCamelCase			=    image_processing(image_inputs[0]			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)

          # Test batched
          __UpperCamelCase			=    image_processing(snake_case			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)







      def    snake_case						(  self       :			List[str]					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random numpy tensors
          __UpperCamelCase			=    prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case			,							numpify=snake_case					)
          for image in image_inputs:
              self.assertIsInstance(snake_case			,							np.ndarray					)

          # Test not batched input
          __UpperCamelCase			=    image_processing(image_inputs[0]			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)

          # Test batched
          __UpperCamelCase			=    image_processing(snake_case			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)







      def    snake_case						(  self       :			int					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          __UpperCamelCase			=    prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case			,							torchify=snake_case					)
          for image in image_inputs:
              self.assertIsInstance(snake_case			,							torch.Tensor					)

          # Test not batched input
          __UpperCamelCase			=    image_processing(image_inputs[0]			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)

          # Test batched
          __UpperCamelCase			=    image_processing(snake_case			,							return_tensors='''pt'''					).pixel_values
          self.assertEqual(
              encoded_images.shape			,							(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)










'''simple docstring'''

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

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


__lowercase:		int    						=  logging.get_logger(__name__)

__lowercase:		Tuple    						=  {
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       List[str]            =       'mbart'
 _lowerCamelCase			:       str            =       ['past_key_values']
 _lowerCamelCase			:       List[str]            =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

 def __init__(			self			:						Tuple,       a_			:						Union[str, Any]=5_0265,       a_			:						Union[str, Any]=1024,       a_			:						Optional[int]=12,       a_			:						str=4096,       a_			:						Optional[Any]=16,       a_			:						str=12,       a_			:						Union[str, Any]=4096,       a_			:						List[Any]=16,       a_			:						Optional[Any]=0.0,       a_			:						int=0.0,       a_			:						Any=True,       a_			:						Optional[Any]=True,       a_			:						Any="gelu",       a_			:						int=1024,       a_			:						Any=0.1,       a_			:						Optional[int]=0.0,       a_			:						Tuple=0.0,       a_			:						Optional[Any]=0.02,       a_			:						Any=0.0,       a_			:						Optional[Any]=False,       a_			:						Any=1,       a_			:						Optional[Any]=0,       a_			:						List[str]=2,       a_			:						Any=2,       **a_			:						int,       ):





    """simple docstring"""



    UpperCamelCase__			    =    vocab_size
    UpperCamelCase__			    =    max_position_embeddings
    UpperCamelCase__			    =    d_model
    UpperCamelCase__			    =    encoder_ffn_dim
    UpperCamelCase__			    =    encoder_layers
    UpperCamelCase__			    =    encoder_attention_heads
    UpperCamelCase__			    =    decoder_ffn_dim
    UpperCamelCase__			    =    decoder_layers
    UpperCamelCase__			    =    decoder_attention_heads
    UpperCamelCase__			    =    dropout
    UpperCamelCase__			    =    attention_dropout
    UpperCamelCase__			    =    activation_dropout
    UpperCamelCase__			    =    activation_function
    UpperCamelCase__			    =    init_std
    UpperCamelCase__			    =    encoder_layerdrop
    UpperCamelCase__			    =    decoder_layerdrop
    UpperCamelCase__			    =    classifier_dropout
    UpperCamelCase__			    =    use_cache
    UpperCamelCase__			    =    encoder_layers
    UpperCamelCase__			    =    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_,       forced_eos_token_id=a_,       **a_,       )







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 @property
 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
       UpperCamelCase__			    =    OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]       )

       if self.use_past:
          UpperCamelCase__			    =    {0: "batch"}
          UpperCamelCase__			    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
          UpperCamelCase__			    =    {0: "batch", 1: "decoder_sequence"}
          UpperCamelCase__			    =    {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
          self.fill_with_past_key_values_(a_,       direction="inputs"       )
    elif self.task == "causal-lm":
       # TODO: figure this case out.
       UpperCamelCase__			    =    OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]       )
       if self.use_past:
          UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
          for i in range(a_       ):
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
    else:
       UpperCamelCase__			    =    OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
               ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
           ]       )

    return common_inputs

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
       UpperCamelCase__			    =    super().outputs
    else:
       UpperCamelCase__			    =    super(a_,       self       ).outputs
       if self.use_past:
          UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
          for i in range(a_       ):
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
    return common_outputs

 def 		lowercase_    (			self			:						str,       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        a_,       a_,       a_,       a_,       a_       )

    # Generate decoder inputs
    UpperCamelCase__			    =    seq_length if not self.use_past else 1
    UpperCamelCase__			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        a_,       a_,       a_,       a_,       a_       )
    UpperCamelCase__			    =    {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
    UpperCamelCase__			    =    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__			    =    common_inputs["input_ids"].shape
       UpperCamelCase__			    =    common_inputs["decoder_input_ids"].shape[1]
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_attention_heads
       UpperCamelCase__			    =    (
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       UpperCamelCase__			    =    decoder_seq_length + 3
       UpperCamelCase__			    =    (
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       UpperCamelCase__			    =    torch.cat(
           [common_inputs["decoder_attention_mask"], torch.ones(a_,       a_       )],       dim=1       )

       UpperCamelCase__			    =    []
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
       UpperCamelCase__			    =    min(a_,       a_       )
       UpperCamelCase__			    =    max(a_,       a_       ) - min_num_layers
       UpperCamelCase__			    =    "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__			    =    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

 def 		lowercase_    (			self			:						Optional[int],       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        a_,       a_,       a_,       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__			    =    common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       UpperCamelCase__			    =    seqlen + 2
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_attention_heads
       UpperCamelCase__			    =    (
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )

       UpperCamelCase__			    =    common_inputs["attention_mask"].dtype
       UpperCamelCase__			    =    torch.cat(
           [common_inputs["attention_mask"], torch.ones(a_,       a_,       dtype=a_       )],       dim=1       )
       UpperCamelCase__			    =    [
           (torch.zeros(a_       ), torch.zeros(a_       )) for _ in range(a_       )
       ]
    return common_inputs

 def 		lowercase_    (			self			:						List[Any],       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    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__			    =    tokenizer.num_special_tokens_to_add(a_       )
    UpperCamelCase__			    =    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__			    =    [" ".join([tokenizer.unk_token]       ) * seq_length] * batch_size
    UpperCamelCase__			    =    dict(tokenizer(a_,       return_tensors=a_       )       )
    return common_inputs

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
       UpperCamelCase__			    =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
           a_,       batch_size=a_,       seq_length=a_,       is_pair=a_,       framework=a_       )

    elif self.task == "causal-lm":
       UpperCamelCase__			    =    self._generate_dummy_inputs_for_causal_lm(
           a_,       batch_size=a_,       seq_length=a_,       is_pair=a_,       framework=a_       )
    else:
       UpperCamelCase__			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           a_,       batch_size=a_,       seq_length=a_,       is_pair=a_,       framework=a_       )

    return common_inputs

 def 		lowercase_    (			self			:						Dict,       a_			:						int,       a_			:						str,       a_			:						List[Any],       a_			:						int       ):





    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
       UpperCamelCase__			    =    super()._flatten_past_key_values_(a_,       a_,       a_,       a_       )
    else:
       UpperCamelCase__			    =    super(a_,       self       )._flatten_past_key_values_(
           a_,       a_,       a_,       a_       )