


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

				_lowercase       :      Tuple	=	"0.12"  # assumed parallelism: 8


if is_torch_available():
				import torch





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Optional[int]=None						) ->   int:





	"""simple docstring"""







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

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

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

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

	return output





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Optional[int]=None						) ->   int:





	"""simple docstring"""







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






@require_flax
class       _UpperCamelCase    :

				"""simple docstring"""







				lowerCAmelCase 		= None
				lowerCAmelCase 		= ()

				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()

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

					A								=				jnp.ones_like(a__   )
					A								=				attention_mask[:max_batch_size, :sequence_length]

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

				@is_pt_flax_cross_test
				def      _UpperCAmelCase					(						self   )	->				Any:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()
					A								=				False
					A								=				max_length
					A								=				0

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				flax_model.generate(a__   ).sequences
						A								=				pt_model.generate(torch.tensor(a__				,   dtype=torch.long   )   )

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

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

				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()
					A								=				False
					A								=				max_length

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				jit(model.generate   )
						A								=				jit_generate(a__   ).sequences

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

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()
					A								=				True
					A								=				max_length

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				jit(model.generate   )
						A								=				jit_generate(a__   ).sequences

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

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()
					A								=				False
					A								=				max_length
					A								=				2

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				jit(model.generate   )
						A								=				jit_generate(a__   ).sequences

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

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()
					A								=				False
					A								=				max_length
					A								=				2
					A								=				2

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()
					A								=				True
					A								=				max_length
					A								=				0.8
					A								=				10
					A								=				0.3
					A								=				1
					A								=				8
					A								=				9

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				jit(model.generate   )
						A								=				jit_generate(a__   ).sequences

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

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()
					A								=				max_length
					A								=				1
					A								=				8
					A								=				9

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				jit(model.generate   )
						A								=				jit_generate(a__   ).sequences

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

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()
					A								=				max_length
					A								=				2
					A								=				1
					A								=				8
					A								=				9

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				jit(model.generate   )
						A								=				jit_generate(a__   ).sequences

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

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()

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

					A								=				False
					A								=				max_length

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				jit(model.generate   )
						A								=				jit_generate(a__				,   attention_mask=a__   ).sequences

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

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()

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

					A								=				True
					A								=				max_length

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				jit(model.generate   )
						A								=				jit_generate(a__				,   attention_mask=a__   ).sequences

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

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A					,			A					,			A					,			A								=				self._get_input_ids_and_config()

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

					A								=				2
					A								=				max_length

					for model_class in self.all_generative_model_classes:
						A								=				model_class(a__   )

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

						A								=				jit(model.generate   )
						A								=				jit_generate(a__				,   attention_mask=a__   ).sequences

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






@require_flax
class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""   )
					A								=				FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""   )

					A								=				"""Hello world"""
					A								=				tokenizer(a__				,   return_tensors="""np"""   ).input_ids

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

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







from __future__ import annotations

from typing import Any






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__				,   a__ = 0   )	->				None:
					A					,			A								=				row, column
					A								=				[[default_value for c in range(a__   )] for r in range(a__   )]

				def __str__(						self   )	->				str:
					A								=				f'Matrix consist of {self.row} rows and {self.column} columns\n'

					# Make string identifier
					A								=				0
					for row_vector in self.array:
						for obj in row_vector:
							A								=				max(a__				,   len(str(a__   )   )   )
					A								=				f'%{max_element_length}s'

					# Make string and return
					def single_line(a__   ) -> str:
						nonlocal string_format_identifier
						A								=				"""["""
						line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
						line += "]"
						return line

					s += "\n".join(single_line(a__   ) for row_vector in self.array   )
					return s

				def __repr__(						self   )	->				str:
					return str(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				bool:
					if not (isinstance(a__				,   (list, tuple)   ) and len(a__   ) == 2):
						return False
					elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
						return False
					else:
						return True

				def __getitem__(						self				,   a__   )	->				Any:
					assert self.validate_indicies(a__   )
					return self.array[loc[0]][loc[1]]

				def __setitem__(						self				,   a__				,   a__   )	->				None:
					assert self.validate_indicies(a__   )
					A								=				value

				def __add__(						self				,   a__   )	->				Matrix:
					assert isinstance(a__				,   a__   )
					assert self.row == another.row and self.column == another.column

					# Add
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c] + another[r, c]
					return result

				def __neg__(						self   )	->				Matrix:
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				-self[r, c]
					return result

				def __sub__(						self				,   a__   )	->				Matrix:
					return self + (-another)

				def __mul__(						self				,   a__   )	->				Matrix:

					if isinstance(a__				,   (int, float)   ):  # Scalar multiplication
						A								=				Matrix(self.row				,   self.column   )
						for r in range(self.row   ):
							for c in range(self.column   ):
								A								=				self[r, c] * another
						return result
					elif isinstance(a__				,   a__   ):  # Matrix multiplication
						assert self.column == another.row
						A								=				Matrix(self.row				,   another.column   )
						for r in range(self.row   ):
							for c in range(another.column   ):
								for i in range(self.column   ):
									result[r, c] += self[r, i] * another[i, c]
						return result
					else:
						A								=				f'Unsupported type given for another ({type(a__   )})'
						raise TypeError(a__   )

				def      _UpperCAmelCase					(						self   )	->				Matrix:
					A								=				Matrix(self.column				,   self.row   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c]
					return result

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Any:
					assert isinstance(a__				,   a__   ) and isinstance(a__				,   a__   )
					assert self.row == self.column == u.row == v.row  # u, v should be column vector
					assert u.column == v.column == 1  # u, v should be column vector

					# Calculate
					A								=				v.transpose()
					A								=				(v_t * self * u)[0, 0] + 1
					if numerator_factor == 0:
						return None  # It's not invertable
					return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					A								=				Matrix(3							,					3							,					0						)
					for i in range(3						):
						A								=				1
					print(f'a^(-1) is {ainv}'						)
					# u, v
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				1, 2, -3
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				4, -2, 5
					print(f'u is {u}'						)
					print(f'v is {v}'						)
					print(f'uv^T is {u * v.transpose()}'						)
					# Sherman Morrison
					print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(UpperCamelCase__							,					UpperCamelCase__						)}'						)





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					import doctest

					doctest.testmod()
				testa()



