
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowerCamelCase_						     =					logging.get_logger(__name__)




class      __A(    enum.Enum			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      0
  SCREAMING_SNAKE_CASE__         =      1






@add_end_docstrings(_a			)
class      __A(    _a			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      """generated"""







  def __init__(self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       super().__init__(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)

       self.check_model_type(
           TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
           if self.framework == """tf"""
           else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_	,      ):
       UpperCamelCase__				=    {}
       if truncation is not None:
            UpperCamelCase__				=    truncation

       UpperCamelCase__				=    generate_kwargs

       UpperCamelCase__				=    {}
       if return_tensors is not None and return_type is None:
            UpperCamelCase__				=    ReturnType.TENSORS if return_tensors else ReturnType.TEXT
       if return_type is not None:
            UpperCamelCase__				=    return_type

       if clean_up_tokenization_spaces is not None:
            UpperCamelCase__				=    clean_up_tokenization_spaces

       if stop_sequence is not None:
            UpperCamelCase__				=    self.tokenizer.encode(SCREAMING_SNAKE_CASE_	,      add_special_tokens=SCREAMING_SNAKE_CASE_						)
            if len(SCREAMING_SNAKE_CASE_						) > 1:
                 warnings.warn(
                     """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
                     """ the stop sequence will be used as the stop sequence string in the interim."""						)
            UpperCamelCase__				=    stop_sequence_ids[0]

       return preprocess_params, forward_params, postprocess_params







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       return True







  def 	UpperCAmelCase_ (self	,      *SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.model.config.prefix if self.model.config.prefix is not None else ''
       if isinstance(args[0]	,      SCREAMING_SNAKE_CASE_						):
            if self.tokenizer.pad_token_id is None:
                 raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""						)
            UpperCamelCase__				=    ([prefix + arg for arg in args[0]],)
            UpperCamelCase__				=    True

       elif isinstance(args[0]	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    (prefix + args[0],)
            UpperCamelCase__				=    False
       else:
            raise ValueError(
                F" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"						)
       UpperCamelCase__				=    self.tokenizer(*SCREAMING_SNAKE_CASE_	,      padding=SCREAMING_SNAKE_CASE_	,      truncation=SCREAMING_SNAKE_CASE_	,      return_tensors=self.framework						)
       # This is produced by tokenizers but is an invalid generate kwargs
       if "token_type_ids" in inputs:
            del inputs["token_type_ids"]
       return inputs







  def __call__(self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    super().__call__(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)
       if (
           isinstance(args[0]	,      SCREAMING_SNAKE_CASE_						)
           and all(isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						) for el in args[0]						)
           and all(len(SCREAMING_SNAKE_CASE_						) == 1 for res in result						)
       ):
            return [res[0] for res in result]
       return result







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=TruncationStrategy.DO_NOT_TRUNCATE	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self._parse_and_tokenize(SCREAMING_SNAKE_CASE_	,      truncation=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)
       return inputs







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):

       if self.framework == "pt":
            UpperCamelCase__				=    model_inputs['input_ids'].shape
       elif self.framework == "tf":
            UpperCamelCase__				=    tf.shape(model_inputs["""input_ids"""]						).numpy()

       UpperCamelCase__				=    generate_kwargs.get("""min_length"""	,      self.model.config.min_length						)
       UpperCamelCase__				=    generate_kwargs.get("""max_length"""	,      self.model.config.max_length						)
       self.check_inputs(SCREAMING_SNAKE_CASE_	,      generate_kwargs["""min_length"""]	,      generate_kwargs["""max_length"""]						)
       UpperCamelCase__				=    self.model.generate(**SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    output_ids.shape[0]
       if self.framework == "pt":
            UpperCamelCase__				=    output_ids.reshape(SCREAMING_SNAKE_CASE_	,      out_b // in_b	,      *output_ids.shape[1:]						)
       elif self.framework == "tf":
            UpperCamelCase__				=    tf.reshape(SCREAMING_SNAKE_CASE_	,      (in_b, out_b // in_b, *output_ids.shape[1:])						)
       return {"output_ids": output_ids}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=ReturnType.TEXT	,      SCREAMING_SNAKE_CASE_=False						):
       UpperCamelCase__				=    []
       for output_ids in model_outputs["output_ids"][0]:
            if return_type == ReturnType.TENSORS:
                 UpperCamelCase__				=    {F"{self.return_name}_token_ids": output_ids}
            elif return_type == ReturnType.TEXT:
                 UpperCamelCase__				=    {
                     F"{self.return_name}_text": self.tokenizer.decode(
                         SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_	,      clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_	,      )
                 }
            records.append(SCREAMING_SNAKE_CASE_						)
       return records






@add_end_docstrings(_a			)
class      __A(    _a			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      """summary"""







  def __call__(self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       return super().__call__(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):

       if max_length < min_length:
            logger.warning(F"Your min_length={min_length} must be inferior than your max_length={max_length}."						)

       if input_length < max_length:
            logger.warning(
                F"Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is "
                """a summarization task, where outputs shorter than the input are typically wanted, you might """
                F"consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})"						)






@add_end_docstrings(_a			)
class      __A(    _a			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      """translation"""







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):

       if input_length > 0.9 * max_length:
            logger.warning(
                F"Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider "
                """increasing your max_length manually, e.g. translator(\'...\', max_length=400)"""						)
       return True







  def 	UpperCAmelCase_ (self	,      *SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=TruncationStrategy.DO_NOT_TRUNCATE	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None						):

       if getattr(self.tokenizer	,      """_build_translation_inputs"""	,      SCREAMING_SNAKE_CASE_						):
            return self.tokenizer._build_translation_inputs(
                *SCREAMING_SNAKE_CASE_	,      return_tensors=self.framework	,      truncation=SCREAMING_SNAKE_CASE_	,      src_lang=SCREAMING_SNAKE_CASE_	,      tgt_lang=SCREAMING_SNAKE_CASE_						)
       else:
            return super()._parse_and_tokenize(*SCREAMING_SNAKE_CASE_	,      truncation=SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    super()._sanitize_parameters(**SCREAMING_SNAKE_CASE_						)
       if src_lang is not None:
            UpperCamelCase__				=    src_lang
       if tgt_lang is not None:
            UpperCamelCase__				=    tgt_lang
       if src_lang is None and tgt_lang is None:
            # Backward compatibility, direct arguments use is preferred.
            UpperCamelCase__				=    kwargs.get("""task"""	,      self.task						)
            UpperCamelCase__				=    task.split("""_"""						)
            if task and len(SCREAMING_SNAKE_CASE_						) == 4:
                 # translation, XX, to YY
                 UpperCamelCase__				=    items[1]
                 UpperCamelCase__				=    items[3]
       return preprocess_params, forward_params, postprocess_params







  def __call__(self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       return super().__call__(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







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 GLPNImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=True	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    do_resize
       UpperCamelCase__				=    size_divisor
       UpperCamelCase__				=    do_rescale







  def 	UpperCAmelCase_ (self						):
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }






@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      GLPNImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    GLPNImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_resize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """size_divisor"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """resample"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_rescale"""						)						)







  def 	UpperCAmelCase_ (self						):
       pass







  def 	UpperCAmelCase_ (self						):
       # 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=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







  def 	UpperCAmelCase_ (self						):
       # 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=SCREAMING_SNAKE_CASE_	,      numpify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







  def 	UpperCAmelCase_ (self						):
       # 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=SCREAMING_SNAKE_CASE_	,      torchify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)





