


'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""
    a													=   MODEL_FOR_CAUSAL_LM_MAPPING
    a													=   TF_MODEL_FOR_CAUSAL_LM_MAPPING


    @require_torch
    def        A   (  self      :							Tuple):
           _A					:     List[str]     = pipeline(task='text-generation'				,							model='sshleifer/tiny-ctrl'				,							framework='pt')
           # Using `do_sample=False` to force deterministic output
           _A					:     Union[str, Any]     = text_generator('This is a test'				,							do_sample=lowercase_)
           self.assertEqual(
               lowercase_				,							[
                   {
                       'generated_text': (
                           'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                           ' oscope. FiliFili@@'
                       )
                   }
               ]				,							)

           _A					:     Optional[Any]     = text_generator(['This is a test', 'This is a second test'])
           self.assertEqual(
               lowercase_				,							[
                   [
                       {
                           'generated_text': (
                               'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                               ' oscope. FiliFili@@'
                           )
                       }
                   ],
                   [
                       {
                           'generated_text': (
                               'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
                               ' oscope. oscope. FiliFili@@'
                           )
                       }
                   ],
               ]				,							)

           _A					:     Dict     = text_generator('This is a test'				,							do_sample=lowercase_				,							num_return_sequences=2				,							return_tensors=lowercase_)
           self.assertEqual(
               lowercase_				,							[
                   {'generated_token_ids': ANY(lowercase_)},
                   {'generated_token_ids': ANY(lowercase_)},
               ]				,							)
           _A					:     Any     = text_generator.model.config.eos_token_id
           _A					:     List[Any]     = """<pad>"""
           _A					:     Dict     = text_generator(
               ['This is a test', 'This is a second test']				,							do_sample=lowercase_				,							num_return_sequences=2				,							batch_size=2				,							return_tensors=lowercase_				,							)
           self.assertEqual(
               lowercase_				,							[
                   [
                       {'generated_token_ids': ANY(lowercase_)},
                       {'generated_token_ids': ANY(lowercase_)},
                   ],
                   [
                       {'generated_token_ids': ANY(lowercase_)},
                       {'generated_token_ids': ANY(lowercase_)},
                   ],
               ]				,							)


    @require_tf
    def        A   (  self      :							Union[str, Any]):
           _A					:     List[str]     = pipeline(task='text-generation'				,							model='sshleifer/tiny-ctrl'				,							framework='tf')

           # Using `do_sample=False` to force deterministic output
           _A					:     Tuple     = text_generator('This is a test'				,							do_sample=lowercase_)
           self.assertEqual(
               lowercase_				,							[
                   {
                       'generated_text': (
                           'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                           ' please,'
                       )
                   }
               ]				,							)

           _A					:     Optional[Any]     = text_generator(['This is a test', 'This is a second test']				,							do_sample=lowercase_)
           self.assertEqual(
               lowercase_				,							[
                   [
                       {
                           'generated_text': (
                               'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                               ' please,'
                           )
                       }
                   ],
                   [
                       {
                           'generated_text': (
                               'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
                               ' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
                           )
                       }
                   ],
               ]				,							)


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							int):
           _A					:     Tuple     = TextGenerationPipeline(model=lowercase_				,							tokenizer=lowercase_)
           return text_generator, ["This is a test", "Another test"]


    def        A   (  self      :							Union[str, Any]):
           _A					:     Optional[int]     = """Hello I believe in"""
           _A					:     Union[str, Any]     = pipeline('text-generation'				,							model='hf-internal-testing/tiny-random-gpt2')
           _A					:     Union[str, Any]     = text_generator(lowercase_)
           self.assertEqual(
               lowercase_				,							[{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}]				,							)

           _A					:     int     = text_generator(lowercase_				,							stop_sequence=' fe')
           self.assertEqual(lowercase_				,							[{'generated_text': 'Hello I believe in fe'}])


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Dict):
           _A					:     Any     = text_generator.model
           _A					:     Tuple     = text_generator.tokenizer

           _A					:     str     = text_generator('This is a test')
           self.assertEqual(lowercase_				,							[{'generated_text': ANY(lowercase_)}])
           self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'))

           _A					:     str     = text_generator('This is a test'				,							return_full_text=lowercase_)
           self.assertEqual(lowercase_				,							[{'generated_text': ANY(lowercase_)}])
           self.assertNotIn('This is a test'				,							outputs[0]['generated_text'])

           _A					:     Tuple     = pipeline(task='text-generation'				,							model=lowercase_				,							tokenizer=lowercase_				,							return_full_text=lowercase_)
           _A					:     Tuple     = text_generator('This is a test')
           self.assertEqual(lowercase_				,							[{'generated_text': ANY(lowercase_)}])
           self.assertNotIn('This is a test'				,							outputs[0]['generated_text'])

           _A					:     Optional[Any]     = text_generator('This is a test'				,							return_full_text=lowercase_)
           self.assertEqual(lowercase_				,							[{'generated_text': ANY(lowercase_)}])
           self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'))

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

           if text_generator.tokenizer.pad_token is not None:
                  _A					:     Optional[Any]     = text_generator(
                      ['This is great !', 'Something else']				,							num_return_sequences=2				,							batch_size=2				,							do_sample=lowercase_)
                  self.assertEqual(
                      lowercase_				,							[
                          [{'generated_text': ANY(lowercase_)}, {'generated_text': ANY(lowercase_)}],
                          [{'generated_text': ANY(lowercase_)}, {'generated_text': ANY(lowercase_)}],
                      ]				,							)

           with self.assertRaises(lowercase_):
                  _A					:     Dict     = text_generator('test'				,							return_full_text=lowercase_				,							return_text=lowercase_)
           with self.assertRaises(lowercase_):
                  _A					:     Union[str, Any]     = text_generator('test'				,							return_full_text=lowercase_				,							return_tensors=lowercase_)
           with self.assertRaises(lowercase_):
                  _A					:     Optional[int]     = text_generator('test'				,							return_text=lowercase_				,							return_tensors=lowercase_)

           # Empty prompt is slighly special
           # it requires BOS token to exist.
           # Special case for Pegasus which will always append EOS so will
           # work even without BOS.
           if (
               text_generator.tokenizer.bos_token_id is not None
               or "Pegasus" in tokenizer.__class__.__name__
               or "Git" in model.__class__.__name__
           ):
                  _A					:     Tuple     = text_generator('')
                  self.assertEqual(lowercase_				,							[{'generated_text': ANY(lowercase_)}])
           else:
                  with self.assertRaises((ValueError, AssertionError)):
                         _A					:     List[Any]     = text_generator('')

           if text_generator.framework == "tf":
                  # TF generation does not support max_new_tokens, and it's impossible
                  # to control long generation with only max_length without
                  # fancy calculation, dismissing tests for now.
                  return
           # We don't care about infinite range models.
           # They already work.
           # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
           _A					:     Tuple     = ["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
           if (
               tokenizer.model_max_length < 10000
               and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
           ):
                  # Handling of large generations
                  with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)):
                         text_generator('This is a test' * 500				,							max_new_tokens=20)

                  _A					:     Union[str, Any]     = text_generator('This is a test' * 500				,							handle_long_generation='hole'				,							max_new_tokens=20)
                  # Hole strategy cannot work
                  with self.assertRaises(lowercase_):
                         text_generator(
                             'This is a test' * 500				,							handle_long_generation='hole'				,							max_new_tokens=tokenizer.model_max_length + 10				,							)


    @require_torch
    @require_accelerate
    @require_torch_gpu
    def        A   (  self      :							Any):
           import torch

           # Classic `model_kwargs`
           _A					:     Any     = pipeline(
               model='hf-internal-testing/tiny-random-bloom'				,							model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa}				,							)
           self.assertEqual(pipe.model.device				,							torch.device(0))
           self.assertEqual(pipe.model.lm_head.weight.dtype				,							torch.bfloataa)
           _A					:     int     = pipe('This is a test')
           self.assertEqual(
               lowercase_				,							[
                   {
                       'generated_text': (
                           'This is a test test test test test test test test test test test test test test test test'
                           ' test'
                       )
                   }
               ]				,							)

           # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
           _A					:     Tuple     = pipeline(model='hf-internal-testing/tiny-random-bloom'				,							device_map='auto'				,							torch_dtype=torch.bfloataa)
           self.assertEqual(pipe.model.device				,							torch.device(0))
           self.assertEqual(pipe.model.lm_head.weight.dtype				,							torch.bfloataa)
           _A					:     Tuple     = pipe('This is a test')
           self.assertEqual(
               lowercase_				,							[
                   {
                       'generated_text': (
                           'This is a test test test test test test test test test test test test test test test test'
                           ' test'
                       )
                   }
               ]				,							)

           # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
           _A					:     Any     = pipeline(model='hf-internal-testing/tiny-random-bloom'				,							device_map='auto')
           self.assertEqual(pipe.model.device				,							torch.device(0))
           self.assertEqual(pipe.model.lm_head.weight.dtype				,							torch.floataa)
           _A					:     Optional[Any]     = pipe('This is a test')
           self.assertEqual(
               lowercase_				,							[
                   {
                       'generated_text': (
                           'This is a test test test test test test test test test test test test test test test test'
                           ' test'
                       )
                   }
               ]				,							)


    @require_torch
    @require_torch_gpu
    def        A   (  self      :							Union[str, Any]):
           import torch

           _A					:     Optional[Any]     = pipeline(model='hf-internal-testing/tiny-random-bloom'				,							device=0				,							torch_dtype=torch.floataa)
           pipe('This is a test')


    @require_torch
    @require_accelerate
    @require_torch_gpu
    def        A   (  self      :							Optional[int]):
           import torch

           _A					:     Any     = pipeline(model='hf-internal-testing/tiny-random-bloom'				,							device_map='auto'				,							torch_dtype=torch.floataa)
           pipe('This is a test'				,							do_sample=lowercase_				,							top_p=0.5)


    def        A   (  self      :							int):
           _A					:     Union[str, Any]     = """Hello world"""
           _A					:     Any     = pipeline('text-generation'				,							model='hf-internal-testing/tiny-random-gpt2')
           if text_generator.model.framework == "tf":
                  _A					:     int     = logging.get_logger('transformers.generation.tf_utils')
           else:
                  _A					:     Union[str, Any]     = logging.get_logger('transformers.generation.utils')
           _A					:     List[Any]     = """Both `max_new_tokens`"""  # The beggining of the message to be checked in this test

           # Both are set by the user -> log warning
           with CaptureLogger(lowercase_) as cl:
                  _A					:     List[Any]     = text_generator(lowercase_				,							max_length=10				,							max_new_tokens=1)
           self.assertIn(lowercase_				,							cl.out)

           # The user only sets one -> no warning
           with CaptureLogger(lowercase_) as cl:
                  _A					:     List[str]     = text_generator(lowercase_				,							max_new_tokens=1)
           self.assertNotIn(lowercase_				,							cl.out)

           with CaptureLogger(lowercase_) as cl:
                  _A					:     Optional[Any]     = text_generator(lowercase_				,							max_length=10)
           self.assertNotIn(lowercase_				,							cl.out)










'''simple docstring'''







from __future__ import annotations


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[list[int]]):
           _A					:     Dict     = TypeError(
               'Matrices must be formed from a list of zero or more lists containing at '
               'least one and the same number of values, each of which must be of type '
               'int or float.')
           if len(SCREAMING_SNAKE_CASE) != 0:
                  _A					:     str     = len(rows[0])
                  if cols == 0:
                         raise error
                  for row in rows:
                         if len(SCREAMING_SNAKE_CASE) != cols:
                                raise error
                         for value in row:
                                if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                                       raise error
                  _A					:     str     = rows
           else:
                  _A					:     Tuple     = []


    def        A   (  self      :							Optional[Any]):
           return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]


    @property
    def        A   (  self      :							List[str]):
           return len(self.rows)


    @property
    def        A   (  self      :							Optional[int]):
           return len(self.rows[0])


    @property
    def        A   (  self      :							Optional[int]):
           return (self.num_rows, self.num_columns)


    @property
    def        A   (  self      :							Any):
           return self.order[0] == self.order[1]


    def        A   (  self      :							int):
           _A					:     Union[str, Any]     = [
               [0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
               for row_num in range(self.num_rows)
           ]
           return Matrix(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]):
           if not self.is_square:
                  return 0
           if self.order == (0, 0):
                  return 1
           if self.order == (1, 1):
                  return int(self.rows[0][0])
           if self.order == (2, 2):
                  return int(
                      (self.rows[0][0] * self.rows[1][1])
                      - (self.rows[0][1] * self.rows[1][0]))
           else:
                  return sum(
                      self.rows[0][column] * self.cofactors().rows[0][column]
                      for column in range(self.num_columns))


    def        A   (  self      :							Tuple):
           return bool(self.determinant())


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           _A					:     str     = [
               [
                   self.rows[other_row][other_column]
                   for other_column in range(self.num_columns)
                   if other_column != column
               ]
               for other_row in range(self.num_rows)
               if other_row != row
           ]
           return Matrix(SCREAMING_SNAKE_CASE).determinant()


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int):
           if (row + column) % 2 == 0:
                  return self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           return -1 * self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           return Matrix(
               [
                   [self.get_minor(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for column in range(self.num_columns)]
                   for row in range(self.num_rows)
               ])


    def        A   (  self      :							Optional[int]):
           return Matrix(
               [
                   [
                       self.minors().rows[row][column]
                       if (row + column) % 2 == 0
                       else self.minors().rows[row][column] * -1
                       for column in range(self.minors().num_columns)
                   ]
                   for row in range(self.minors().num_rows)
               ])


    def        A   (  self      :							Tuple):
           _A					:     str     = [
               [self.cofactors().rows[column][row] for column in range(self.num_columns)]
               for row in range(self.num_rows)
           ]
           return Matrix(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple):
           _A					:     str     = self.determinant()
           if not determinant:
                  raise TypeError('Only matrices with a non-zero determinant have an inverse')
           return self.adjugate() * (1 / determinant)


    def __repr__(  self      :							str):
           return str(self.rows)


    def __str__(  self      :							Optional[int]):
           if self.num_rows == 0:
                  return "[]"
           if self.num_rows == 1:
                  return "[[" + ". ".join(str(self.rows[0])) + "]]"
           return (
               "["
               + "\n ".join(
                   [
                       '[' + '. '.join([str(SCREAMING_SNAKE_CASE) for value in row]) + '.]'
                       for row in self.rows
                   ])
               + "]"
           )


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							int | None = None):
           _A					:     Tuple     = TypeError('Row must be a list containing all ints and/or floats')
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise type_error
           for value in row:
                  if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                         raise type_error
           if len(SCREAMING_SNAKE_CASE) != self.num_columns:
                  raise ValueError(
                      'Row must be equal in length to the other rows in the matrix')
           if position is None:
                  self.rows.append(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     List[str]     = self.rows[0:position] + [row] + self.rows[position:]


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							int | None = None):
           _A					:     int     = TypeError(
               'Column must be a list containing all ints and/or floats')
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise type_error
           for value in column:
                  if not isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                         raise type_error
           if len(SCREAMING_SNAKE_CASE) != self.num_rows:
                  raise ValueError(
                      'Column must be equal in length to the other columns in the matrix')
           if position is None:
                  _A					:     str     = [self.rows[i] + [column[i]] for i in range(self.num_rows)]
           else:
                  _A					:     Dict     = [
                      self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                      for i in range(self.num_rows)
                  ]


    def __eq__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							object):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  return NotImplemented
           return self.rows == other.rows


    def __ne__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							object):
           return not self == other


    def __neg__(  self      :							Optional[Any]):
           return self * -1


    def __add__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Matrix):
           if self.order != other.order:
                  raise ValueError('Addition requires matrices of the same order')
           return Matrix(
               [
                   [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
                   for i in range(self.num_rows)
               ])


    def __sub__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Matrix):
           if self.order != other.order:
                  raise ValueError('Subtraction requires matrices of the same order')
           return Matrix(
               [
                   [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
                   for i in range(self.num_rows)
               ])


    def __mul__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Matrix | int | float):
           if isinstance(SCREAMING_SNAKE_CASE				,							(int, float)):
                  return Matrix(
                      [[int(element * other) for element in row] for row in self.rows])
           elif isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  if self.num_columns != other.num_rows:
                         raise ValueError(
                             'The number of columns in the first matrix must '
                             'be equal to the number of rows in the second')
                  return Matrix(
                      [
                          [Matrix.dot_product(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for column in other.columns()]
                          for row in self.rows
                      ])
           else:
                  raise TypeError(
                      'A Matrix can only be multiplied by an int, float, or another matrix')


    def __pow__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise TypeError('A Matrix can only be raised to the power of an int')
           if not self.is_square:
                  raise ValueError('Only square matrices can be raised to a power')
           if other == 0:
                  return self.identity()
           if other < 0:
                  if self.is_invertable():
                         return self.inverse() ** (-other)
                  raise ValueError(
                      'Only invertable matrices can be raised to a negative power')
           _A					:     Union[str, Any]     = self
           for _ in range(other - 1):
                  result *= self
           return result


    @classmethod
    def        A   (  cls      :							List[str]				,							SCREAMING_SNAKE_CASE      :							list[int]				,							SCREAMING_SNAKE_CASE      :							list[int]):
           return sum(row[i] * column[i] for i in range(len(SCREAMING_SNAKE_CASE)))


if __name__ == "__main__":
   import doctest

   doctest.testmod()






