


import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
   import jax.numpy as jnp

if is_tf_available():
   import tensorflow as tf

if is_torch_available():
   import torch
class 			_a  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		Any							=  {
                 '''task_specific_params''': {
                     '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_2_8, '''min_length''': 1_2, '''num_beams''': 4},
                     '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_4_2, '''min_length''': 5_6, '''num_beams''': 4},
                     '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 6_2, '''min_length''': 1_1, '''num_beams''': 6},
                 }
             }
             a_ :		Tuple							=  {
                 '''task_specific_params.summarization.length_penalty''': 1.0,
                 '''task_specific_params.summarization.max_length''': 1_2_8,
                 '''task_specific_params.summarization.min_length''': 1_2,
                 '''task_specific_params.summarization.num_beams''': 4,
                 '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
                 '''task_specific_params.summarization_cnn.max_length''': 1_4_2,
                 '''task_specific_params.summarization_cnn.min_length''': 5_6,
                 '''task_specific_params.summarization_cnn.num_beams''': 4,
                 '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
                 '''task_specific_params.summarization_xsum.max_length''': 6_2,
                 '''task_specific_params.summarization_xsum.min_length''': 1_1,
                 '''task_specific_params.summarization_xsum.num_beams''': 6,
             }

             self.assertEqual(flatten_dict(snake_case_					)					,							snake_case_					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Optional[int]:
             a_ :		List[Any]							=  np.random.randn(3					,							4					)
             self.assertTrue(np.allclose(transpose(snake_case_					)					,							x.transpose()					)					)

             a_ :		Union[str, Any]							=  np.random.randn(3					,							4					,							5					)
             self.assertTrue(np.allclose(transpose(snake_case_					,							axes=(1, 2, 0)					)					,							x.transpose((1, 2, 0)					)					)					)
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[str]:
             a_ :		List[Any]							=  np.random.randn(3					,							4					)
             a_ :		str							=  torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(transpose(snake_case_					)					,							transpose(snake_case_					).numpy()					)					)

             a_ :		Optional[Any]							=  np.random.randn(3					,							4					,							5					)
             a_ :		Any							=  torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(transpose(snake_case_					,							axes=(1, 2, 0)					)					,							transpose(snake_case_					,							axes=(1, 2, 0)					).numpy()					)					)
      @require_tf
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Optional[int]:
             a_ :		Any							=  np.random.randn(3					,							4					)
             a_ :		int							=  tf.constant(snake_case_					)
             self.assertTrue(np.allclose(transpose(snake_case_					)					,							transpose(snake_case_					).numpy()					)					)

             a_ :		Optional[Any]							=  np.random.randn(3					,							4					,							5					)
             a_ :		Dict							=  tf.constant(snake_case_					)
             self.assertTrue(np.allclose(transpose(snake_case_					,							axes=(1, 2, 0)					)					,							transpose(snake_case_					,							axes=(1, 2, 0)					).numpy()					)					)
      @require_flax
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Any:
             a_ :		Optional[Any]							=  np.random.randn(3					,							4					)
             a_ :		List[str]							=  jnp.array(snake_case_					)
             self.assertTrue(np.allclose(transpose(snake_case_					)					,							np.asarray(transpose(snake_case_					)					)					)					)

             a_ :		int							=  np.random.randn(3					,							4					,							5					)
             a_ :		Optional[int]							=  jnp.array(snake_case_					)
             self.assertTrue(np.allclose(transpose(snake_case_					,							axes=(1, 2, 0)					)					,							np.asarray(transpose(snake_case_					,							axes=(1, 2, 0)					)					)					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Union[str, Any]:
             a_ :		Optional[Any]							=  np.random.randn(3					,							4					)
             self.assertTrue(np.allclose(reshape(snake_case_					,							(4, 3)					)					,							np.reshape(snake_case_					,							(4, 3)					)					)					)

             a_ :		Any							=  np.random.randn(3					,							4					,							5					)
             self.assertTrue(np.allclose(reshape(snake_case_					,							(1_2, 5)					)					,							np.reshape(snake_case_					,							(1_2, 5)					)					)					)
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      List[Any]:
             a_ :		Union[str, Any]							=  np.random.randn(3					,							4					)
             a_ :		str							=  torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(reshape(snake_case_					,							(4, 3)					)					,							reshape(snake_case_					,							(4, 3)					).numpy()					)					)

             a_ :		List[Any]							=  np.random.randn(3					,							4					,							5					)
             a_ :		int							=  torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(reshape(snake_case_					,							(1_2, 5)					)					,							reshape(snake_case_					,							(1_2, 5)					).numpy()					)					)
      @require_tf
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[Any]:
             a_ :		Union[str, Any]							=  np.random.randn(3					,							4					)
             a_ :		Dict							=  tf.constant(snake_case_					)
             self.assertTrue(np.allclose(reshape(snake_case_					,							(4, 3)					)					,							reshape(snake_case_					,							(4, 3)					).numpy()					)					)

             a_ :		Tuple							=  np.random.randn(3					,							4					,							5					)
             a_ :		Optional[Any]							=  tf.constant(snake_case_					)
             self.assertTrue(np.allclose(reshape(snake_case_					,							(1_2, 5)					)					,							reshape(snake_case_					,							(1_2, 5)					).numpy()					)					)
      @require_flax
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[str]:
             a_ :		Optional[Any]							=  np.random.randn(3					,							4					)
             a_ :		List[Any]							=  jnp.array(snake_case_					)
             self.assertTrue(np.allclose(reshape(snake_case_					,							(4, 3)					)					,							np.asarray(reshape(snake_case_					,							(4, 3)					)					)					)					)

             a_ :		Any							=  np.random.randn(3					,							4					,							5					)
             a_ :		Optional[Any]							=  jnp.array(snake_case_					)
             self.assertTrue(np.allclose(reshape(snake_case_					,							(1_2, 5)					)					,							np.asarray(reshape(snake_case_					,							(1_2, 5)					)					)					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[str]:
             a_ :		str							=  np.random.randn(1					,							3					,							4					)
             self.assertTrue(np.allclose(squeeze(snake_case_					)					,							np.squeeze(snake_case_					)					)					)

             a_ :		Dict							=  np.random.randn(1					,							4					,							1					,							5					)
             self.assertTrue(np.allclose(squeeze(snake_case_					,							axis=2					)					,							np.squeeze(snake_case_					,							axis=2					)					)					)
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Tuple:
             a_ :		Optional[int]							=  np.random.randn(1					,							3					,							4					)
             a_ :		List[Any]							=  torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(squeeze(snake_case_					)					,							squeeze(snake_case_					).numpy()					)					)

             a_ :		Tuple							=  np.random.randn(1					,							4					,							1					,							5					)
             a_ :		Optional[int]							=  torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(squeeze(snake_case_					,							axis=2					)					,							squeeze(snake_case_					,							axis=2					).numpy()					)					)
      @require_tf
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Union[str, Any]:
             a_ :		str							=  np.random.randn(1					,							3					,							4					)
             a_ :		int							=  tf.constant(snake_case_					)
             self.assertTrue(np.allclose(squeeze(snake_case_					)					,							squeeze(snake_case_					).numpy()					)					)

             a_ :		Optional[Any]							=  np.random.randn(1					,							4					,							1					,							5					)
             a_ :		Any							=  tf.constant(snake_case_					)
             self.assertTrue(np.allclose(squeeze(snake_case_					,							axis=2					)					,							squeeze(snake_case_					,							axis=2					).numpy()					)					)
      @require_flax
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Any:
             a_ :		Tuple							=  np.random.randn(1					,							3					,							4					)
             a_ :		str							=  jnp.array(snake_case_					)
             self.assertTrue(np.allclose(squeeze(snake_case_					)					,							np.asarray(squeeze(snake_case_					)					)					)					)

             a_ :		Union[str, Any]							=  np.random.randn(1					,							4					,							1					,							5					)
             a_ :		Dict							=  jnp.array(snake_case_					)
             self.assertTrue(np.allclose(squeeze(snake_case_					,							axis=2					)					,							np.asarray(squeeze(snake_case_					,							axis=2					)					)					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Union[str, Any]:
             a_ :		Any							=  np.random.randn(3					,							4					)
             self.assertTrue(np.allclose(expand_dims(snake_case_					,							axis=1					)					,							np.expand_dims(snake_case_					,							axis=1					)					)					)
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Any:
             a_ :		Dict							=  np.random.randn(3					,							4					)
             a_ :		Any							=  torch.tensor(snake_case_					)
             self.assertTrue(np.allclose(expand_dims(snake_case_					,							axis=1					)					,							expand_dims(snake_case_					,							axis=1					).numpy()					)					)
      @require_tf
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[Any]:
             a_ :		Union[str, Any]							=  np.random.randn(3					,							4					)
             a_ :		Optional[Any]							=  tf.constant(snake_case_					)
             self.assertTrue(np.allclose(expand_dims(snake_case_					,							axis=1					)					,							expand_dims(snake_case_					,							axis=1					).numpy()					)					)

      @require_flax
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Optional[Any]:
             a_ :		Dict							=  np.random.randn(3					,							4					)
             a_ :		str							=  jnp.array(snake_case_					)
             self.assertTrue(np.allclose(expand_dims(snake_case_					,							axis=1					)					,							np.asarray(expand_dims(snake_case_					,							axis=1					)					)					)					)






import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]=""					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="train"					)		->      Tuple:
             assert os.path.isdir(SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  []
             a_ :		Optional[int]							=  os.listdir(SCREAMING_SNAKE_CASE__					)
             for story_filename in story_filenames_list:
                    if "summary" in story_filename:
                           continue
                    a_ :		List[str]							=  os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                    if not os.path.isfile(SCREAMING_SNAKE_CASE__					):
                           continue
                    self.documents.append(SCREAMING_SNAKE_CASE__					)
      def __len__(	self    :	Dict					)		->      str:
             return len(self.documents					)

      def __getitem__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					)		->      str:
             a_ :		int							=  self.documents[idx]
             a_ :		Tuple							=  document_path.split('/'					)[-1]
             with open(SCREAMING_SNAKE_CASE__					,							encoding='utf-8'					) as source:
                    a_ :		Dict							=  source.read()
                    a_							,		a_ :		Optional[Any]							=  process_story(SCREAMING_SNAKE_CASE__					)
             return document_name, story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[Any]							=  list(filter(lambda __A    : len(__A    ) != 0  ,   [line.strip() for line in raw_story.split('\n'    )]    )    )

       # for some unknown reason some lines miss a period, add it
       a_ :		List[Any]							=  [_add_missing_period(__A    ) for line in nonempty_lines]

       # gather article lines
       a_ :		int							=  []
       a_ :		List[Any]							=  deque(__A    )
       while True:
              try:
                     a_ :		Dict							=  lines.popleft()
                     if element.startswith('@highlight'    ):
                            break
                     story_lines.append(__A    )
              except IndexError:
                     # if "@highlight" is absent from the file we pop
                     # all elements until there is None, raising an exception.
                     return story_lines, []

    # gather summary lines
       a_ :		List[str]							=  list(filter(lambda __A    : not t.startswith('@highlight'    )  ,   __A    )    )

       return story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]    )     ->		Any:

       """simple docstring"""

       a_ :		Any							=  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
       if line.startswith('@highlight'    ):
              return line
       if line[-1] in END_TOKENS:
              return line
       return line + "."







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Union[str, Any]  ,   __A			:      List[str]    )     ->		Union[str, Any]:

       """simple docstring"""

       if len(__A    ) > block_size:
              return sequence[:block_size]
       else:
              sequence.extend([pad_token_id] * (block_size - len(__A    ))    )
              return sequence







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      str    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  torch.ones_like(__A    )
       a_ :		List[str]							=  sequence == pad_token_id
       a_ :		str							=  0
       return mask







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      Optional[Any]  ,   __A			:      Dict    )     ->		List[str]:

       """simple docstring"""

       a_ :		Optional[int]							=  [tokenizer.encode(__A    ) for line in story_lines]
       a_ :		int							=  [token for sentence in story_lines_token_ids for token in sentence]
       a_ :		Dict							=  [tokenizer.encode(__A    ) for line in summary_lines]
       a_ :		int							=  [token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      List[str]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		int							=  []
       for sequence in batch:
              a_ :		int							=  -1
              a_ :		Dict							=  []
              for s in sequence:
                     if s == separator_token_id:
                            sentence_num += 1
                     embeddings.append(sentence_num % 2    )
              batch_embeddings.append(__A    )
       return torch.tensor(__A    )


