
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       =					'''0.12'''  # assumed parallelism: 8


if is_torch_available():
	import torch






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=None				):
	if rng is None:
		lowercase	    =       random.Random()

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

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

	lowercase	    =       np.array(__SCREAMING_SNAKE_CASE					,				dtype=jnp.intaa				).reshape(__SCREAMING_SNAKE_CASE				)

	return output






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=None				):
	lowercase	    =       ids_tensor(__SCREAMING_SNAKE_CASE					,				vocab_size=2					,				rng=__SCREAMING_SNAKE_CASE				)
	# make sure that at least one token is attended to for each batch
	lowercase	    =       1
	return attn_mask







@require_flax
class 			A_  :




	'''simple docstring'''


	_UpperCamelCase	:	int									=			None
	_UpperCamelCase	:	Any									=			()





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase	    =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

		# generate max 5 tokens
		lowercase	    =       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()`
			lowercase	    =       config.eos_token_id
		return config, input_ids, attention_mask, max_length





	@is_pt_flax_cross_test
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()
		lowercase	    =       False
		lowercase	    =       max_length
		lowercase	    =       0

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

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()
		lowercase	    =       False
		lowercase	    =       max_length

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()
		lowercase	    =       True
		lowercase	    =       max_length

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()
		lowercase	    =       False
		lowercase	    =       max_length
		lowercase	    =       2

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()
		lowercase	    =       False
		lowercase	    =       max_length
		lowercase	    =       2
		lowercase	    =       2

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

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





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

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()
		lowercase	    =       max_length
		lowercase	    =       1
		lowercase	    =       8
		lowercase	    =       9

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()
		lowercase	    =       max_length
		lowercase	    =       2
		lowercase	    =       1
		lowercase	    =       8
		lowercase	    =       9

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()

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

		lowercase	    =       False
		lowercase	    =       max_length

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()

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

		lowercase	    =       True
		lowercase	    =       max_length

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

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       self._get_input_ids_and_config()

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

		lowercase	    =       2
		lowercase	    =       max_length

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

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

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

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







@require_flax
class 			A_  (     unittest.TestCase  ):




	'''simple docstring'''


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

		lowercase	    =       'Hello world'
		lowercase	    =       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'			):
			lowercase	    =       {'foo': 'bar'}
			model.generate(snake_case						,       **snake_case			)








"""simple docstring"""


import random
from typing import Any




def        lowercase__     (       lowercase_      )       ->		list[Any]:




      """simple docstring"""

      for _ in range(len(lowercase_      )      ):
            _UpperCamelCase   :						Dict    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase   :						str    =							random.randint(0		,len(lowercase_      ) - 1      )
            _UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							data[b], data[a]
      return data


if __name__ == "__main__":
  lowerCamelCase__						= [0, 1, 2, 3, 4, 5, 6, 7]
  lowerCamelCase__						= ["python", "says", "hello", "!"]
  print("Fisher-Yates Shuffle:")
  print("List", integers, strings)
  print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
