


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

SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger(__name__)


class      __lowerCAmelCase  (			enum.Enum       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							0
  _SCREAMING_SNAKE_CASE		     =							1






@add_end_docstrings(a       )
class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'generated'



  def __init__(  self							:							List[str]     ,					*_lowerCAmelCase							:							Tuple     ,					**_lowerCAmelCase							:							Union[str, Any]	) ->			List[str]:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)

     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        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Dict=None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							List[str]=None     ,					**_lowerCAmelCase							:							Dict     ,					) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       {}
     if truncation is not None:
        snake_case_            =       truncation

     snake_case_            =       generate_kwargs

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

     if clean_up_tokenization_spaces is not None:
        snake_case_            =       clean_up_tokenization_spaces

     if stop_sequence is not None:
        snake_case_            =       self.tokenizer.encode(_lowerCAmelCase     ,					add_special_tokens=_lowerCAmelCase	)
        if len(_lowerCAmelCase	) > 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."	)
        snake_case_            =       stop_sequence_ids[0]

     return preprocess_params, forward_params, postprocess_params



  def        lowerCAmelCase__	(  self							:							Optional[int]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int	) ->			int:




     """simple docstring"""
     return True



  def        lowerCAmelCase__	(  self							:							str     ,					*_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							Optional[Any]	) ->			List[str]:




     """simple docstring"""
     snake_case_            =       self.model.config.prefix if self.model.config.prefix is not None else ""
     if isinstance(args[0]     ,					_lowerCAmelCase	):
        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"	)
        snake_case_            =       ([prefix + arg for arg in args[0]],)
        snake_case_            =       True

     elif isinstance(args[0]     ,					_lowerCAmelCase	):
        snake_case_            =       (prefix + args[0],)
        snake_case_            =       False
     else:
        raise ValueError(
            F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''	)
     snake_case_            =       self.tokenizer(*_lowerCAmelCase     ,					padding=_lowerCAmelCase     ,					truncation=_lowerCAmelCase     ,					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							:							Any     ,					*_lowerCAmelCase							:							List[str]     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			Any:




     """simple docstring"""
     snake_case_            =       super().__call__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)
     if (
         isinstance(args[0]     ,					_lowerCAmelCase	)
         and all(isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) for el in args[0]	)
         and all(len(_lowerCAmelCase	) == 1 for res in result	)
     ):
        return [res[0] for res in result]
     return result



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							int=TruncationStrategy.DO_NOT_TRUNCATE     ,					**_lowerCAmelCase							:							Union[str, Any]	) ->			Dict:




     """simple docstring"""
     snake_case_            =       self._parse_and_tokenize(_lowerCAmelCase     ,					truncation=_lowerCAmelCase     ,					**_lowerCAmelCase	)
     return inputs



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Union[str, Any]	) ->			str:




     """simple docstring"""
     if self.framework == "pt":
        snake_case_     ,     snake_case_            =       model_inputs["input_ids"].shape
     elif self.framework == "tf":
        snake_case_     ,     snake_case_            =       tf.shape(model_inputs["input_ids"]	).numpy()

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



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Union[str, Any]=ReturnType.TEXT     ,					_lowerCAmelCase							:							Union[str, Any]=False	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       []
     for output_ids in model_outputs["output_ids"][0]:
        if return_type == ReturnType.TENSORS:
           snake_case_            =       {F'''{self.return_name}_token_ids''': output_ids}
        elif return_type == ReturnType.TEXT:
           snake_case_            =       {
               F'''{self.return_name}_text''': self.tokenizer.decode(
                   _lowerCAmelCase     ,					skip_special_tokens=_lowerCAmelCase     ,					clean_up_tokenization_spaces=_lowerCAmelCase     ,					)
           }
        records.append(_lowerCAmelCase	)
     return records






@add_end_docstrings(a       )
class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'summary'



  def __call__(  self							:							Optional[int]     ,					*_lowerCAmelCase							:							List[str]     ,					**_lowerCAmelCase							:							Dict	) ->			Dict:




     """simple docstring"""
     return super().__call__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int	) ->			bool:




     """simple docstring"""
     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      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'translation'



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int	) ->			Tuple:




     """simple docstring"""
     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        lowerCAmelCase__	(  self							:							Optional[int]     ,					*_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							List[Any]=TruncationStrategy.DO_NOT_TRUNCATE     ,					_lowerCAmelCase							:							str=None     ,					_lowerCAmelCase							:							Tuple=None	) ->			List[str]:




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



  def        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							Optional[Any]=None     ,					_lowerCAmelCase							:							List[Any]=None     ,					**_lowerCAmelCase							:							Any	) ->			str:




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



  def __call__(  self							:							Dict     ,					*_lowerCAmelCase							:							int     ,					**_lowerCAmelCase							:							Dict	) ->			Union[str, Any]:




     """simple docstring"""
     return super().__call__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)








import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor


class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]=7     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							Dict=1_8     ,					_lowerCAmelCase							:							Tuple=3_0     ,					_lowerCAmelCase							:							Optional[int]=4_0_0     ,					_lowerCAmelCase							:							Optional[int]=True     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							List[Any]=True     ,					) ->			Dict:




     """simple docstring"""
     snake_case_            =       size if size is not None else {"height": 1_8, "width": 1_8}
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       num_channels
     snake_case_            =       image_size
     snake_case_            =       min_resolution
     snake_case_            =       max_resolution
     snake_case_            =       do_resize
     snake_case_            =       size
     snake_case_            =       do_normalize



  def        lowerCAmelCase__	(  self							:							str	) ->			Optional[int]:




     """simple docstring"""
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                 [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
             ]	),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }






@require_torch
@require_vision
class      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							ImageGPTImageProcessor if is_vision_available() else None



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessingTester(self	)



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			Union[str, Any]:




     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"clusters"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_resize"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"size"	)	)
     self.assertTrue(hasattr(_lowerCAmelCase     ,					"do_normalize"	)	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size     ,					{"height": 1_8, "width": 1_8}	)

     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2	)
     self.assertEqual(image_processor.size     ,					{"height": 4_2, "width": 4_2}	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     snake_case_            =       json.loads(image_processor.to_json_string()	)
     for key, value in self.image_processor_dict.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					obj[key]	)	)
        else:
           self.assertEqual(obj[key]     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        snake_case_            =       os.path.join(_lowerCAmelCase     ,					"image_processor.json"	)
        image_processor_first.to_json_file(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_json_file(_lowerCAmelCase	).to_dict()

     snake_case_            =       image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					image_processor_second[key]	)	)
        else:
           self.assertEqual(image_processor_first[key]     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        image_processor_first.save_pretrained(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_pretrained(_lowerCAmelCase	).to_dict()

     snake_case_            =       image_processor_first.to_dict()
     for key, value in image_processor_first.items():
        if key == "clusters":
           self.assertTrue(np.array_equal(_lowerCAmelCase     ,					image_processor_second[key]	)	)
        else:
           self.assertEqual(image_processor_first[key]     ,					_lowerCAmelCase	)



  @unittest.skip("ImageGPT requires clusters at initialization"	)
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     pass






def       _lowerCAmelCase    (				)->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       load_dataset("hf-internal-testing/fixtures_image_utils"		,    split="test"				)

   snake_case_            =       Image.open(dataset[4]["file"]				)
   snake_case_            =       Image.open(dataset[5]["file"]				)

   snake_case_            =       [imagea, imagea]

   return images



@require_vision
@require_torch
class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  @slow
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"	)

     snake_case_            =       prepare_images()

     # test non-batched
     snake_case_            =       image_processing(images[0]     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(1, 1_0_2_4)	)

     snake_case_            =       [3_0_6, 1_9_1, 1_9_1]
     self.assertEqual(encoding.input_ids[0, :3].tolist()     ,					_lowerCAmelCase	)

     # test batched
     snake_case_            =       image_processing(_lowerCAmelCase     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(2, 1_0_2_4)	)

     snake_case_            =       [3_0_3, 1_3, 1_3]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,					_lowerCAmelCase	)




