
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


__magic_name__  :  Tuple     			=   logging.get_logger(__name__)

__magic_name__  :  List[str]     			=   {
    '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 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  lowerCAmelCase_								=		"""mbart"""
  lowerCAmelCase_								=		["""past_key_values"""]
  lowerCAmelCase_								=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

  def __init__(					self						,    __UpperCamelCase=50265						,    __UpperCamelCase=1024						,    __UpperCamelCase=12						,    __UpperCamelCase=4096						,    __UpperCamelCase=16						,    __UpperCamelCase=12						,    __UpperCamelCase=4096						,    __UpperCamelCase=16						,    __UpperCamelCase=0.0						,    __UpperCamelCase=0.0						,    __UpperCamelCase=True						,    __UpperCamelCase=True						,    __UpperCamelCase="gelu"						,    __UpperCamelCase=1024						,    __UpperCamelCase=0.1						,    __UpperCamelCase=0.0						,    __UpperCamelCase=0.0						,    __UpperCamelCase=0.02						,    __UpperCamelCase=0.0						,    __UpperCamelCase=False						,    __UpperCamelCase=1						,    __UpperCamelCase=0						,    __UpperCamelCase=2						,    __UpperCamelCase=2						,    **__UpperCamelCase						,    ):
      A_								=					vocab_size
      A_								=					max_position_embeddings
      A_								=					d_model
      A_								=					encoder_ffn_dim
      A_								=					encoder_layers
      A_								=					encoder_attention_heads
      A_								=					decoder_ffn_dim
      A_								=					decoder_layers
      A_								=					decoder_attention_heads
      A_								=					dropout
      A_								=					attention_dropout
      A_								=					activation_dropout
      A_								=					activation_function
      A_								=					init_std
      A_								=					encoder_layerdrop
      A_								=					decoder_layerdrop
      A_								=					classifier_dropout
      A_								=					use_cache
      A_								=					encoder_layers
      A_								=					scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=__UpperCamelCase						,    bos_token_id=__UpperCamelCase						,    eos_token_id=__UpperCamelCase						,    is_encoder_decoder=__UpperCamelCase						,    forced_eos_token_id=__UpperCamelCase						,    **__UpperCamelCase						,    )



class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





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

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

          if self.use_past:
              self.fill_with_past_key_values_(__UpperCamelCase						,    direction="inputs"					)
      elif self.task == "causal-lm":
          # TODO: figure this case out.
          A_								=					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]					)
          if self.use_past:
              A_	,	A_								=					self.num_layers
              for i in range(__UpperCamelCase					):
                  A_								=					{0: "batch", 2: "past_sequence + sequence"}
                  A_								=					{0: "batch", 2: "past_sequence + sequence"}
      else:
          A_								=					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					):
      if self.task in ["default", "seq2seq-lm"]:
          A_								=					super().outputs
      else:
          A_								=					super(__UpperCamelCase						,    self					).outputs
          if self.use_past:
              A_	,	A_								=					self.num_layers
              for i in range(__UpperCamelCase					):
                  A_								=					{0: "batch", 2: "past_sequence + sequence"}
                  A_								=					{0: "batch", 2: "past_sequence + sequence"}
      return common_outputs

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = -1						,    __UpperCamelCase = -1						,    __UpperCamelCase = False						,    __UpperCamelCase = None						,    ):
      A_								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)

      # Generate decoder inputs
      A_								=					seq_length if not self.use_past else 1
      A_								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)
      A_								=					{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
      A_								=					dict(**__UpperCamelCase						,    **__UpperCamelCase					)

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

          A_								=					torch.cat(
              [common_inputs["decoder_attention_mask"], torch.ones(__UpperCamelCase						,    __UpperCamelCase					)]						,    dim=1					)

          A_								=					[]
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          A_	,	A_								=					self.num_layers
          A_								=					min(__UpperCamelCase						,    __UpperCamelCase					)
          A_								=					max(__UpperCamelCase						,    __UpperCamelCase					) - min_num_layers
          A_								=					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = -1						,    __UpperCamelCase = -1						,    __UpperCamelCase = False						,    __UpperCamelCase = None						,    ):
      A_								=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)

      if self.use_past:
          if not is_torch_available():
              raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
          else:
              import torch
          A_	,	A_								=					common_inputs["input_ids"].shape
          # Not using the same length for past_key_values
          A_								=					seqlen + 2
          A_	,	A_								=					self.num_layers
          A_	,	A_								=					self.num_attention_heads
          A_								=					(
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )

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

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = -1						,    __UpperCamelCase = -1						,    __UpperCamelCase = False						,    __UpperCamelCase = 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
      A_								=					compute_effective_axis_dimension(
          __UpperCamelCase						,    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
      A_								=					tokenizer.num_special_tokens_to_add(__UpperCamelCase					)
      A_								=					compute_effective_axis_dimension(
          __UpperCamelCase						,    fixed_dimension=OnnxConfig.default_fixed_sequence						,    num_token_to_add=__UpperCamelCase					)

      # Generate dummy inputs according to compute batch and sequence
      A_								=					[" ".join([tokenizer.unk_token]					) * seq_length] * batch_size
      A_								=					dict(tokenizer(__UpperCamelCase						,    return_tensors=__UpperCamelCase					)					)
      return common_inputs

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = -1						,    __UpperCamelCase = -1						,    __UpperCamelCase = False						,    __UpperCamelCase = None						,    ):
      if self.task in ["default", "seq2seq-lm"]:
          A_								=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              __UpperCamelCase						,    batch_size=__UpperCamelCase						,    seq_length=__UpperCamelCase						,    is_pair=__UpperCamelCase						,    framework=__UpperCamelCase					)

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

      return common_inputs

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      if self.task in ["default", "seq2seq-lm"]:
          A_								=					super()._flatten_past_key_values_(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)
      else:
          A_								=					super(__UpperCamelCase						,    self					)._flatten_past_key_values_(
              __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)








import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image


class 							lowerCamelCase       (		unittest.TestCase			,  __snake_case					):



  """simple docstring"""





  def 					lowercase_						(					self					):
      A_								=					load_tool("image-question-answering"					)
      self.tool.setup()
      A_								=					load_tool("image-question-answering"						,    remote=__UpperCamelCase					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.tool(__UpperCamelCase						,    "How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.remote_tool(__UpperCamelCase						,    "How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.tool(image=__UpperCamelCase						,    question="How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)

  def 					lowercase_						(					self					):
      A_								=					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      A_								=					self.remote_tool(image=__UpperCamelCase						,    question="How many cats are sleeping on the couch?"					)
      self.assertEqual(__UpperCamelCase						,    "2"					)






