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

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


_SCREAMING_SNAKE_CASE     				=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     				=		{
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class 							a				(     __lowerCAmelCase			):







 """simple docstring"""






 lowerCamelCase      :Tuple            =						'''blenderbot-small'''
 lowerCamelCase      :Union[str, Any]            =						['''past_key_values''']
 lowerCamelCase      :List[str]            =						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



 def __init__(   self						,							lowerCAmelCase_=5_02_65						,							lowerCAmelCase_=5_12						,							lowerCAmelCase_=8						,							lowerCAmelCase_=20_48						,							lowerCAmelCase_=16						,							lowerCAmelCase_=8						,							lowerCAmelCase_=20_48						,							lowerCAmelCase_=16						,							lowerCAmelCase_=0.0						,							lowerCAmelCase_=0.0						,							lowerCAmelCase_=True						,							lowerCAmelCase_=True						,							lowerCAmelCase_="gelu"						,							lowerCAmelCase_=5_12						,							lowerCAmelCase_=0.1						,							lowerCAmelCase_=0.0						,							lowerCAmelCase_=0.0						,							lowerCAmelCase_=0.02						,							lowerCAmelCase_=1						,							lowerCAmelCase_=False						,							lowerCAmelCase_=0						,							lowerCAmelCase_=1						,							lowerCAmelCase_=2						,							lowerCAmelCase_=2						,							**lowerCAmelCase_						,							)    ->   Union[str, Any]:
        _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	  =    use_cache
        _A	  =    encoder_layers
        _A	  =    scale_embedding  # scale factor will be sqrt(d_model) if True

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



class 							a				(     __lowerCAmelCase			):







 """simple docstring"""






 @property
 def 	UpperCAmelCase				(   self				)    ->   Mapping[str, Mapping[int, str]]:




        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_(__SCREAMING_SNAKE_CASE						,							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(__SCREAMING_SNAKE_CASE				):
                             _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 	UpperCAmelCase				(   self				)    ->   Mapping[str, Mapping[int, str]]:




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



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = -1						,							lowerCAmelCase_ = -1						,							lowerCAmelCase_ = False						,							lowerCAmelCase_ = None						,							)    ->   Mapping[str, Any]:
        _A	  =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)

        # Generate decoder inputs
        _A	  =    seq_length if not self.use_past else 1
        _A	  =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)
        _A	  =    {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
        _A	  =    dict(**__SCREAMING_SNAKE_CASE						,							**__SCREAMING_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
               _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(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)]						,							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(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				)
               _A	  =    max(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE				) - min_num_layers
               _A	  =    """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = -1						,							lowerCAmelCase_ = -1						,							lowerCAmelCase_ = False						,							lowerCAmelCase_ = None						,							)    ->   Mapping[str, Any]:
        _A	  =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							__SCREAMING_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
               _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(__SCREAMING_SNAKE_CASE						,							__SCREAMING_SNAKE_CASE						,							dtype=__SCREAMING_SNAKE_CASE				)]						,							dim=1				)
               _A	  =    [
                   (torch.zeros(__SCREAMING_SNAKE_CASE				), torch.zeros(__SCREAMING_SNAKE_CASE				)) for _ in range(__SCREAMING_SNAKE_CASE				)
               ]
        return common_inputs



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = -1						,							lowerCAmelCase_ = -1						,							lowerCAmelCase_ = False						,							lowerCAmelCase_ = None						,							)    ->   Mapping[str, Any]:
        _A	  =    compute_effective_axis_dimension(
            __SCREAMING_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
        _A	  =    tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE				)
        _A	  =    compute_effective_axis_dimension(
            __SCREAMING_SNAKE_CASE						,							fixed_dimension=OnnxConfig.default_fixed_sequence						,							num_token_to_add=__SCREAMING_SNAKE_CASE				)

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



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = -1						,							lowerCAmelCase_ = -1						,							lowerCAmelCase_ = False						,							lowerCAmelCase_ = None						,							)    ->   Mapping[str, Any]:




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

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

        return common_inputs



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   Optional[Any]:




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







'''simple docstring'''



import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = IFInpaintingPipeline
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__       = PipelineTesterMixin.required_optional_params - {"latents"}
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Dict:


      """simple docstring"""




      return self._get_dummy_components()
 def  UpperCAmelCase__						(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict=0     )    ->					Any:


      """simple docstring"""




      if str(__SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
           __SCREAMING_SNAKE_CASE      = torch.manual_seed(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }

      return inputs
 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available()   ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,				)
 def  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != """cuda"""   ,				reason="""float16 requires CUDA"""     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					int:


      """simple docstring"""




      super().test_save_load_floataa(expected_max_diff=1E-1     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					List[Any]:


      """simple docstring"""




      self._test_save_load_local()


 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2   ,				)






