'''simple docstring'''






from typing import List

import numpy as np





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			dict				):
     _A    		=  {key: len(__snake_case				) for key, value in gen_kwargs.items() if isinstance(__snake_case   ,		__snake_case				)}
     if len(set(lists_lengths.values()				)				) > 1:
          raise RuntimeError(
              (
                  'Sharding is ambiguous for this dataset: '
                  + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                  + '\n'.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items()				)
                  + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                  + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
              )				)
     _A    		=  max(lists_lengths.values()   ,		default=0				)
     return max(1   ,		__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			int				):
     _A    		=  []
     for group_idx in range(__snake_case				):
          _A    		=  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
          if num_shards_to_add == 0:
               break
          _A    		=  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
          _A    		=  range(__snake_case   ,		start + num_shards_to_add				)
          shards_indices_per_group.append(__snake_case				)
     return shards_indices_per_group





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			dict   ,		__snake_case							:			int				):
     _A    		=  _number_of_shards_in_gen_kwargs(__snake_case				)
     if num_shards == 1:
          return [dict(__snake_case				)]
     else:
          _A    		=  _distribute_shards(num_shards=__snake_case   ,		max_num_jobs=__snake_case				)
          return [
              {
                  key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                  if isinstance(__snake_case   ,		__snake_case				)
                  else value
                  for key, value in gen_kwargs.items()
              }
              for group_idx in range(len(__snake_case				)				)
          ]





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[dict]				):
     return {
         key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
         if isinstance(gen_kwargs_list[0][key]   ,		__snake_case				)
         else gen_kwargs_list[0][key]
         for key in gen_kwargs_list[0]
     }





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			np.random.Generator   ,		__snake_case							:			dict				):
     _A    		=  {len(__snake_case				) for value in gen_kwargs.values() if isinstance(__snake_case   ,		__snake_case				)}
     _A    		=  {}
     for size in list_sizes:
          _A    		=  list(range(__snake_case				)				)
          rng.shuffle(indices_per_size[size]				)
     # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
     _A    		=  dict(__snake_case				)
     for key, value in shuffled_kwargs.items():
          if isinstance(__snake_case   ,		__snake_case				):
               _A    		=  [value[i] for i in indices_per_size[len(__snake_case				)]]
     return shuffled_kwargs




'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
   import torch







@is_pipeline_test
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __lowerCAmelCase         =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			List[str]:
        _A    		=  TextaTextGenerationPipeline(model=UpperCamelCase__, tokenizer=UpperCamelCase__     )
        return generator, ["Something to write", "Something else"]
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        _A    		=  generator('Something there'     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ANY(UpperCamelCase__     )}]     )
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'     )     )

        _A    		=  generator(['This is great !', 'Something else'], num_return_sequences=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        _A    		=  generator(
            ['This is great !', 'Something else'], num_return_sequences=2, batch_size=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        with self.assertRaises(UpperCamelCase__     ):
             generator(4     )
   @require_torch
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='pt'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )

        _A    		=  3
        _A    		=  generator(
            'Something there', num_return_sequences=UpperCamelCase__, num_beams=UpperCamelCase__, )
        _A    		=  [
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': ''},
        ]
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  generator('This is a test', do_sample=UpperCamelCase__, num_return_sequences=2, return_tensors=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                {'generated_token_ids': ANY(torch.Tensor     )},
                {'generated_token_ids': ANY(torch.Tensor     )},
            ], )
        _A    		=  generator.model.config.eos_token_id
        _A    		=  '<pad>'
        _A    		=  generator(
            ['This is a test', 'This is a second test'], do_sample=UpperCamelCase__, num_return_sequences=2, batch_size=2, return_tensors=UpperCamelCase__, )
        self.assertEqual(
            UpperCamelCase__, [
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
            ], )

   @require_tf
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='tf'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )



