



import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.generation import (
     FlaxForcedBOSTokenLogitsProcessor,
     FlaxForcedEOSTokenLogitsProcessor,
     FlaxLogitsProcessorList,
     FlaxMinLengthLogitsProcessor,
     FlaxTemperatureLogitsWarper,
     FlaxTopKLogitsWarper,
     FlaxTopPLogitsWarper,
 )

@require_flax
class     _A       (	unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           __a						       =				jnp.ones((batch_size, length)) / length
           return scores



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				None
           __a						       =				20

           __a						       =				self._get_uniform_logits(batch_size=2     ,		length=__SCREAMING_SNAKE_CASE)

           # tweak scores to not be uniform anymore
           __a						       =				scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
           __a						       =				scores.at[1, 10].set((1 / length) - 0.4)  # valley, 1st batch

           # compute softmax
           __a						       =				jax.nn.softmax(__SCREAMING_SNAKE_CASE     ,		axis=-1)

           __a						       =				FlaxTemperatureLogitsWarper(temperature=0.5)
           __a						       =				FlaxTemperatureLogitsWarper(temperature=1.3)

           __a						       =				jax.nn.softmax(temp_dist_warper_sharper(__SCREAMING_SNAKE_CASE     ,		scores.copy()     ,		cur_len=__SCREAMING_SNAKE_CASE)     ,		axis=-1)
           __a						       =				jax.nn.softmax(temp_dist_warper_smoother(__SCREAMING_SNAKE_CASE     ,		scores.copy()     ,		cur_len=__SCREAMING_SNAKE_CASE)     ,		axis=-1)

           # uniform distribution stays uniform
           self.assertTrue(jnp.allclose(probs[0, :]     ,		warped_prob_sharp[0, :]     ,		atol=1E-3))
           self.assertTrue(jnp.allclose(probs[0, :]     ,		warped_prob_smooth[0, :]     ,		atol=1E-3))

           # sharp peaks get higher, valleys get lower
           self.assertLess(probs[1, :].max()     ,		warped_prob_sharp[1, :].max())
           self.assertGreater(probs[1, :].min()     ,		warped_prob_sharp[1, :].min())

           # smooth peaks get lower, valleys get higher
           self.assertGreater(probs[1, :].max()     ,		warped_prob_smooth[1, :].max())
           self.assertLess(probs[1, :].min()     ,		warped_prob_smooth[1, :].min())



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				None
           __a						       =				10
           __a						       =				2

           # create ramp distribution
           __a						       =				np.broadcast_to(np.arange(__SCREAMING_SNAKE_CASE)[None, :]     ,		(batch_size, vocab_size)).copy()
           __a						       =				ramp_logits[1:, : vocab_size // 2] + vocab_size

           __a						       =				FlaxTopKLogitsWarper(3)

           __a						       =				top_k_warp(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)

           # check that correct tokens are filtered
           self.assertListEqual(jnp.isinf(scores[0]).tolist()     ,		7 * [True] + 3 * [False])
           self.assertListEqual(jnp.isinf(scores[1]).tolist()     ,		2 * [True] + 3 * [False] + 5 * [True])

           # check special case
           __a						       =				5
           __a						       =				FlaxTopKLogitsWarper(top_k=1     ,		filter_value=0.0     ,		min_tokens_to_keep=3)

           __a						       =				np.broadcast_to(np.arange(__SCREAMING_SNAKE_CASE)[None, :]     ,		(batch_size, length)).copy()
           __a						       =				top_k_warp_safety_check(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)

           # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
           self.assertListEqual((scores == 0.0).sum(axis=-1).tolist()     ,		[2, 2])



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				None
           __a						       =				10
           __a						       =				2

           # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
           __a						       =				np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

           __a						       =				FlaxTopPLogitsWarper(0.8)
           __a						       =				np.exp(top_p_warp(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE))

           # dist should be filtered to keep min num values so that sum is >= top_p
           # exp (-inf) => 0
           __a						       =				np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		atol=1E-3))

           # check edge cases with negative and extreme logits
           __a						       =				np.broadcast_to(np.arange(__SCREAMING_SNAKE_CASE)[None, :]     ,		(batch_size, vocab_size)).copy() - (
               vocab_size // 2
           )

           # make ramp_logits more extreme
           __a						       =				ramp_logits[1] * 1_00.0

           # make sure at least 2 tokens are kept
           __a						       =				FlaxTopPLogitsWarper(0.9     ,		min_tokens_to_keep=2     ,		filter_value=0.0)
           __a						       =				top_p_warp(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)

           # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
           self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist()     ,		[3, 2])



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				20
           __a						       =				4
           __a						       =				0

           __a						       =				FlaxMinLengthLogitsProcessor(min_length=10     ,		eos_token_id=__SCREAMING_SNAKE_CASE)

           # check that min length is applied at length 5
           __a						       =				ids_tensor((batch_size, 20)     ,		vocab_size=20)
           __a						       =				5
           __a						       =				self._get_uniform_logits(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				min_dist_processor(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()     ,		4 * [-float('''inf''')])

           # check that min length is not applied anymore at length 15
           __a						       =				self._get_uniform_logits(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				15
           __a						       =				min_dist_processor(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           self.assertFalse(jnp.isinf(__SCREAMING_SNAKE_CASE).any())



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				20
           __a						       =				4
           __a						       =				0

           __a						       =				FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__SCREAMING_SNAKE_CASE)

           # check that all scores are -inf except the bos_token_id score
           __a						       =				ids_tensor((batch_size, 1)     ,		vocab_size=20)
           __a						       =				1
           __a						       =				self._get_uniform_logits(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				logits_processor(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
           self.assertListEqual(scores[:, bos_token_id].tolist()     ,		4 * [0])  # score for bos_token_id shold be zero

           # check that bos_token_id is not forced if current length is greater than 1
           __a						       =				3
           __a						       =				self._get_uniform_logits(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				logits_processor(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           self.assertFalse(jnp.isinf(__SCREAMING_SNAKE_CASE).any())



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				20
           __a						       =				4
           __a						       =				0
           __a						       =				5

           __a						       =				FlaxForcedEOSTokenLogitsProcessor(max_length=__SCREAMING_SNAKE_CASE     ,		eos_token_id=__SCREAMING_SNAKE_CASE)

           # check that all scores are -inf except the eos_token_id when max_length is reached
           __a						       =				ids_tensor((batch_size, 4)     ,		vocab_size=20)
           __a						       =				4
           __a						       =				self._get_uniform_logits(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				logits_processor(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
           self.assertListEqual(scores[:, eos_token_id].tolist()     ,		4 * [0])  # score for eos_token_id should be zero

           # check that eos_token_id is not forced if max_length is not reached
           __a						       =				3
           __a						       =				self._get_uniform_logits(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				logits_processor(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           self.assertFalse(jnp.isinf(__SCREAMING_SNAKE_CASE).any())



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				4
           __a						       =				10
           __a						       =				15
           __a						       =				2
           __a						       =				1
           __a						       =				15

           # dummy input_ids and scores
           __a						       =				ids_tensor((batch_size, sequence_length)     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				input_ids.copy()

           __a						       =				self._get_uniform_logits(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				scores.copy()

           # instantiate all dist processors
           __a						       =				FlaxTemperatureLogitsWarper(temperature=0.5)
           __a						       =				FlaxTopKLogitsWarper(3)
           __a						       =				FlaxTopPLogitsWarper(0.8)

           # instantiate all logits processors
           __a						       =				FlaxMinLengthLogitsProcessor(min_length=10     ,		eos_token_id=__SCREAMING_SNAKE_CASE)
           __a						       =				FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__SCREAMING_SNAKE_CASE)
           __a						       =				FlaxForcedEOSTokenLogitsProcessor(max_length=__SCREAMING_SNAKE_CASE     ,		eos_token_id=__SCREAMING_SNAKE_CASE)

           __a						       =				10

           # no processor list
           __a						       =				temp_dist_warp(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           __a						       =				top_k_warp(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           __a						       =				top_p_warp(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           __a						       =				min_dist_proc(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           __a						       =				bos_dist_proc(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
           __a						       =				eos_dist_proc(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)

           # with processor list
           __a						       =				FlaxLogitsProcessorList(
               [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
           __a						       =				processor(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)

           # scores should be equal
           self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		atol=1E-3))

           # input_ids should never be changed
           self.assertListEqual(input_ids.tolist()     ,		input_ids_comp.tolist())





       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				4
           __a						       =				10
           __a						       =				15
           __a						       =				2
           __a						       =				1
           __a						       =				15

           # dummy input_ids and scores
           __a						       =				ids_tensor((batch_size, sequence_length)     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				input_ids.copy()

           __a						       =				self._get_uniform_logits(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				scores.copy()

           # instantiate all dist processors
           __a						       =				FlaxTemperatureLogitsWarper(temperature=0.5)
           __a						       =				FlaxTopKLogitsWarper(3)
           __a						       =				FlaxTopPLogitsWarper(0.8)

           # instantiate all logits processors
           __a						       =				FlaxMinLengthLogitsProcessor(min_length=10     ,		eos_token_id=__SCREAMING_SNAKE_CASE)
           __a						       =				FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__SCREAMING_SNAKE_CASE)
           __a						       =				FlaxForcedEOSTokenLogitsProcessor(max_length=__SCREAMING_SNAKE_CASE     ,		eos_token_id=__SCREAMING_SNAKE_CASE)

           __a						       =				10

           # no processor list
           def run_no_processor_list(__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):
               __a						       =				temp_dist_warp(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
               __a						       =				top_k_warp(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
               __a						       =				top_p_warp(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
               __a						       =				min_dist_proc(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
               __a						       =				bos_dist_proc(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
               __a						       =				eos_dist_proc(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
               return scores

           # with processor list
           def run_processor_list(__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):
               __a						       =				FlaxLogitsProcessorList(
                   [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
               __a						       =				processor(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		cur_len=__SCREAMING_SNAKE_CASE)
               return scores

           __a						       =				jax.jit(__SCREAMING_SNAKE_CASE)
           __a						       =				jax.jit(__SCREAMING_SNAKE_CASE)

           __a						       =				jitted_run_no_processor_list(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				jitted_run_processor_list(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

           # scores should be equal
           self.assertTrue(jnp.allclose(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		atol=1E-3))

           # input_ids should never be changed
           self.assertListEqual(input_ids.tolist()     ,		input_ids_comp.tolist())










from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests





def      __snake_case	(			_UpperCAmelCase = "isbn/0140328726"				):
    __a						       =				olid.strip().strip('''/'''				)  # Remove leading/trailing whitespace & slashes
    if new_olid.count('''/'''				) != 1:
        __a						       =				f'{olid} is not a valid Open Library olid'
        raise ValueError(_UpperCAmelCase				)
    return requests.get(f'https://openlibrary.org/{new_olid}.json'				).json()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				{
        '''title''': '''Title''',
        '''publish_date''': '''Publish date''',
        '''authors''': '''Authors''',
        '''number_of_pages''': '''Number of pages:''',
        '''first_sentence''': '''First sentence''',
        '''isbn_10''': '''ISBN (10)''',
        '''isbn_13''': '''ISBN (13)''',
    }
    __a						       =				{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
    __a						       =				[
        get_openlibrary_data(author['''key''']				)['''name'''] for author in data['''Authors''']
    ]
    __a						       =				data['''First sentence''']['''value''']
    for key, value in data.items():
        if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
            __a						       =				''', '''.join(_UpperCAmelCase				)
    return data


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 while True:
  __snake_case						:List[Any]      		=     input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
  if isbn.lower() in ("", "q", "quit", "exit", "stop"):
   break

  if len(isbn) not in (10, 13) or not isbn.isdigit():
   print(f'Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.')
   continue

  print(f'\nSearching Open Library for ISBN: {isbn}...\n')

  try:
   __snake_case						:Optional[Any]      		=     summarize_book(get_openlibrary_data(f'isbn/{isbn}'))
   print('''\n'''.join(f'{key}: {value}' for key, value in book_summary.items()))
  except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
   print(f'Sorry, there are no results for ISBN: {isbn}.')





