

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
					import os

					import jax.numpy as jnp
					from jax import jit

					from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
					from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

					_UpperCAmelCase		:   List[Any]										=       """0.12"""  # assumed parallelism: 8


if is_torch_available():
					import torch





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__=None      ):







							'''simple docstring'''






							if rng is None:
														snake_case_							=    random.Random()

							snake_case_							=    1
							for dim in shape:
														total_dims *= dim

							snake_case_							=    []
							for _ in range(UpperCamelCase__      ):
														values.append(rng.randint(0   ,							vocab_size - 1      )      )

							snake_case_							=    np.array(UpperCamelCase__   ,							dtype=jnp.intaa      ).reshape(UpperCamelCase__      )

							return output





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__=None      ):







							'''simple docstring'''






							snake_case_							=    ids_tensor(UpperCamelCase__   ,							vocab_size=2   ,							rng=UpperCamelCase__      )
							# make sure that at least one token is attended to for each batch
							snake_case_							=    1
							return attn_mask







@require_flax
class  lowercase				:
				__SCREAMING_SNAKE_CASE					: Any       			=			None
				__SCREAMING_SNAKE_CASE					: List[str]       			=			()




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

											# cut to half length & take max batch_size 3
											snake_case_							=    2
											snake_case_							=    inputs['input_ids'].shape[-1] // 2
											snake_case_							=    inputs['input_ids'][:max_batch_size, :sequence_length]

											snake_case_							=    jnp.ones_like(snake_case       )
											snake_case_							=    attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											snake_case_							=    input_ids.shape[-1] + 5
											if config.eos_token_id is not None and config.pad_token_id is None:
																		# hack to allow generate for models such as GPT2 as is done in `generate()`
																		snake_case_							=    config.eos_token_id
											return config, input_ids, attention_mask, max_length




				@is_pt_flax_cross_test
				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()
											snake_case_							=    False
											snake_case_							=    max_length
											snake_case_							=    0

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		snake_case_							=    getattr(snake_case      ,   snake_case       )
																		snake_case_							=    pt_model_class(snake_case       ).eval()
																		snake_case_							=    load_flax_weights_in_pytorch_model(snake_case      ,   flax_model.params       )

																		snake_case_							=    flax_model.generate(snake_case       ).sequences
																		snake_case_							=    pt_model.generate(torch.tensor(snake_case      ,   dtype=torch.long       )       )

																		if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																									snake_case_							=    flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																		self.assertListEqual(pt_generation_outputs.numpy().tolist()      ,   flax_generation_outputs.tolist()       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()
											snake_case_							=    False
											snake_case_							=    max_length

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[-1]      ,   snake_case       )

																		snake_case_							=    jit(model.generate       )
																		snake_case_							=    jit_generate(snake_case       ).sequences

																		self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()
											snake_case_							=    True
											snake_case_							=    max_length

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[-1]      ,   snake_case       )

																		snake_case_							=    jit(model.generate       )
																		snake_case_							=    jit_generate(snake_case       ).sequences

																		self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()
											snake_case_							=    False
											snake_case_							=    max_length
											snake_case_							=    2

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[-1]      ,   snake_case       )

																		snake_case_							=    jit(model.generate       )
																		snake_case_							=    jit_generate(snake_case       ).sequences

																		self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()
											snake_case_							=    False
											snake_case_							=    max_length
											snake_case_							=    2
											snake_case_							=    2

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[0]      ,   input_ids.shape[0] * config.num_return_sequences       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()
											snake_case_							=    True
											snake_case_							=    max_length
											snake_case_							=    0.8
											snake_case_							=    10
											snake_case_							=    0.3
											snake_case_							=    1
											snake_case_							=    8
											snake_case_							=    9

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[-1]      ,   snake_case       )

																		snake_case_							=    jit(model.generate       )
																		snake_case_							=    jit_generate(snake_case       ).sequences

																		self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()
											snake_case_							=    max_length
											snake_case_							=    1
											snake_case_							=    8
											snake_case_							=    9

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[-1]      ,   snake_case       )

																		snake_case_							=    jit(model.generate       )
																		snake_case_							=    jit_generate(snake_case       ).sequences

																		self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()
											snake_case_							=    max_length
											snake_case_							=    2
											snake_case_							=    1
											snake_case_							=    8
											snake_case_							=    9

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[-1]      ,   snake_case       )

																		snake_case_							=    jit(model.generate       )
																		snake_case_							=    jit_generate(snake_case       ).sequences

																		self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()

											# pad attention mask on the left
											snake_case_							=    attention_mask.at[(0, 0)].set(0       )

											snake_case_							=    False
											snake_case_							=    max_length

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case      ,   attention_mask=snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[-1]      ,   snake_case       )

																		snake_case_							=    jit(model.generate       )
																		snake_case_							=    jit_generate(snake_case      ,   attention_mask=snake_case       ).sequences

																		self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()

											# pad attention mask on the left
											snake_case_							=    attention_mask.at[(0, 0)].set(0       )

											snake_case_							=    True
											snake_case_							=    max_length

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case      ,   attention_mask=snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[-1]      ,   snake_case       )

																		snake_case_							=    jit(model.generate       )
																		snake_case_							=    jit_generate(snake_case      ,   attention_mask=snake_case       ).sequences

																		self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()       )




				def     a							(					self       ):
											snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    self._get_input_ids_and_config()

											# pad attention mask on the left
											snake_case_							=    attention_mask.at[(0, 0)].set(0       )

											snake_case_							=    2
											snake_case_							=    max_length

											for model_class in self.all_generative_model_classes:
																		snake_case_							=    model_class(snake_case       )

																		snake_case_							=    model.generate(snake_case      ,   attention_mask=snake_case       ).sequences
																		self.assertEqual(generation_outputs.shape[-1]      ,   snake_case       )

																		snake_case_							=    jit(model.generate       )
																		snake_case_							=    jit_generate(snake_case      ,   attention_mask=snake_case       ).sequences

																		self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()       )







@require_flax
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											snake_case_							=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'       )
											snake_case_							=    FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'       )

											snake_case_							=    'Hello world'
											snake_case_							=    tokenizer(snake_case      ,   return_tensors='np'       ).input_ids

											# typos are quickly detected (the correct argument is `do_sample`)
											with self.assertRaisesRegex(snake_case      ,   'do_samples'       ):
																		model.generate(snake_case      ,   do_samples=snake_case       )

											# arbitrary arguments that will not be used anywhere are also not accepted
											with self.assertRaisesRegex(snake_case      ,   'foo'       ):
																		snake_case_							=    {'foo': 'bar'}
																		model.generate(snake_case      ,   **snake_case       )







def 					__lowerCamelCase	( ):







							'''simple docstring'''






							return [list(range(1000 - i   ,							-1000 - i   ,							-1      )      ) for i in range(1000      )]


_UpperCAmelCase		:   Union[str, Any]										=       generate_large_matrix()
_UpperCAmelCase		:   Tuple										=       (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							assert all(row == sorted(UpperCamelCase__   ,							reverse=UpperCamelCase__      ) for row in grid      )
							assert all(list(UpperCamelCase__      ) == sorted(UpperCamelCase__   ,							reverse=UpperCamelCase__      ) for col in zip(*UpperCamelCase__      )      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    len(UpperCamelCase__      ) - 1

							# Edge cases such as no values or all numbers are negative.
							if not array or array[0] < 0:
														return 0

							while right + 1 > left:
														snake_case_							=    (left + right) // 2
														snake_case_							=    array[mid]

														# Num must be negative and the index must be greater than or equal to 0.
														if num < 0 and array[mid - 1] >= 0:
																					return mid

														if num >= 0:
																					snake_case_							=    mid + 1
														else:
																					snake_case_							=    mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
							return len(UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    len(grid[0]      )

							for i in range(len(UpperCamelCase__      )      ):
														snake_case_							=    find_negative_index(grid[i][:bound]      )
														total += bound
							return (len(UpperCamelCase__      ) * len(grid[0]      )) - total





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return len([number for row in grid for number in row if number < 0]      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							for row in grid:
														for i, number in enumerate(UpperCamelCase__      ):
																					if number < 0:
																												total += len(UpperCamelCase__      ) - i
																												break
							return total





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							from timeit import timeit

							print('Running benchmarks'      )
							snake_case_							=    (
							    'from __main__ import count_negatives_binary_search, '
							    'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
							)
							for func in (
							    "count_negatives_binary_search",  # took 0.7727 seconds
							    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
							    "count_negatives_brute_force",  # took 12.8160 seconds
							):
														snake_case_							=    timeit(F'''{func}(grid=grid)'''   ,							setup=UpperCamelCase__   ,							number=500      )
														print(F'''{func}() took {time:0.4f} seconds'''      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()




