"""simple docstring"""



import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


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

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
					import torch

@require_flax
class SCREAMING_SNAKE_CASE     :







	"""simple docstring"""


	def __init__(   self		:      List[str]					,   _snake_case		:      Any					,   _snake_case		:      Dict=14					,   _snake_case		:      List[Any]=7					,   _snake_case		:      Union[str, Any]=True					,   _snake_case		:      Tuple=True					,   _snake_case		:      Optional[Any]=True					,   _snake_case		:      Optional[int]=99					,   _snake_case		:      Dict=32					,   _snake_case		:      str=5					,   _snake_case		:      Union[str, Any]=4					,   _snake_case		:      int=37					,   _snake_case		:      List[Any]="gelu"					,   _snake_case		:      List[Any]=0.1					,   _snake_case		:      int=0.1					,   _snake_case		:      Dict=512					,   _snake_case		:      Tuple=0.02					,   _snake_case		:      str=None					,   )				->			Dict:




				'''simple docstring'''






				a__     				=		parent
				a__     				=		batch_size
				a__     				=		seq_length
				a__     				=		is_training
				a__     				=		use_input_mask
				a__     				=		use_labels
				a__     				=		vocab_size
				a__     				=		d_model
				a__     				=		num_hidden_layers
				a__     				=		num_attention_heads
				a__     				=		ffn_dim
				a__     				=		activation_function
				a__     				=		activation_dropout
				a__     				=		attention_dropout
				a__     				=		max_position_embeddings
				a__     				=		initializer_range
				a__     				=		None
				a__     				=		0
				a__     				=		2
				a__     				=		1


	def        _lowerCAmelCase				(   self		:      List[str]				)				->			str:




				'''simple docstring'''






				a__     				=		np.clip(ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size				)					,   3					,   self.vocab_size				)

				a__     				=		None
				if self.use_input_mask:
							a__     				=		random_attention_mask([self.batch_size, self.seq_length]				)

				a__     				=		XGLMConfig(
				    vocab_size=self.vocab_size					,   d_model=self.hidden_size					,   num_layers=self.num_hidden_layers					,   attention_heads=self.num_attention_heads					,   ffn_dim=self.ffn_dim					,   activation_function=self.activation_function					,   activation_dropout=self.activation_dropout					,   attention_dropout=self.attention_dropout					,   max_position_embeddings=self.max_position_embeddings					,   initializer_range=self.initializer_range					,   use_cache=_snake_case					,   bos_token_id=self.bos_token_id					,   eos_token_id=self.eos_token_id					,   pad_token_id=self.pad_token_id					,   )

				return (config, input_ids, input_mask)


	def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Dict:




				'''simple docstring'''






				a__     				=		self.prepare_config_and_inputs()
				a__				,       a__				,       a__     				=		config_and_inputs
				a__     				=		{'input_ids': input_ids, 'attention_mask': attention_mask}
				return config, inputs_dict


	def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			List[Any]:




				'''simple docstring'''






				a__				,       a__				,       a__     				=		self.prepare_config_and_inputs()

				a__     				=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
				a__     				=		ids_tensor([self.batch_size, self.seq_length]					,   vocab_size=2				)

				return (
				    config,
				    input_ids,
				    attention_mask,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)


	def        _lowerCAmelCase				(   self		:      List[str]					,   _snake_case		:      List[str]					,   _snake_case		:      List[Any]					,   _snake_case		:      Dict					,   _snake_case		:      str				)				->			List[str]:




				'''simple docstring'''






				a__     				=		20
				a__     				=		model_class_name(_snake_case				)

				a__     				=		model.init_cache(input_ids.shape[0]					,   _snake_case				)
				a__     				=		jnp.ones((input_ids.shape[0], max_decoder_length)					,   dtype='i4'				)

				a__     				=		jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1				)[None, :]					,   (input_ids.shape[0], input_ids.shape[-1] - 1)				)
				a__     				=		model(
				    input_ids[:, :-1]					,   attention_mask=_snake_case					,   past_key_values=_snake_case					,   position_ids=_snake_case					,   )

				a__     				=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,   dtype='i4'				)
				a__     				=		model(
				    input_ids[:, -1:]					,   attention_mask=_snake_case					,   past_key_values=outputs_cache.past_key_values					,   position_ids=_snake_case					,   )

				a__     				=		model(_snake_case				)

				a__     				=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
				self.parent.assertTrue(diff < 1E-3					,   msg=F'''Max diff is {diff}'''				)



	def        _lowerCAmelCase				(   self		:      int					,   _snake_case		:      Optional[Any]					,   _snake_case		:      Tuple					,   _snake_case		:      Union[str, Any]					,   _snake_case		:      Optional[Any]				)				->			List[str]:




				'''simple docstring'''






				a__     				=		20
				a__     				=		model_class_name(_snake_case				)

				a__     				=		jnp.concatenate(
				    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]					,   axis=-1					,   )

				a__     				=		model.init_cache(input_ids.shape[0]					,   _snake_case				)
				a__     				=		jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1				)[None, :]					,   (input_ids.shape[0], input_ids.shape[-1] - 1)				)

				a__     				=		model(
				    input_ids[:, :-1]					,   attention_mask=_snake_case					,   past_key_values=_snake_case					,   position_ids=_snake_case					,   )
				a__     				=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,   dtype='i4'				)
				a__     				=		model(
				    input_ids[:, -1:]					,   past_key_values=outputs_cache.past_key_values					,   attention_mask=_snake_case					,   position_ids=_snake_case					,   )

				a__     				=		model(_snake_case					,   attention_mask=_snake_case				)
				a__     				=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
				self.parent.assertTrue(diff < 1E-3					,   msg=F'''Max diff is {diff}'''				)

@require_sentencepiece
@require_flax
class SCREAMING_SNAKE_CASE     (    a   ,      a   ,      unittest.TestCase	):







	"""simple docstring"""


	a_       :	int  			=(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
	a_       :	Tuple  			=(FlaxXGLMForCausalLM,) if is_flax_available() else ()


	def        _lowerCAmelCase				(   self		:      int				)				->			List[str]:




				'''simple docstring'''






				a__     				=		FlaxXGLMModelTester(self				)


	def        _lowerCAmelCase				(   self		:      List[Any]				)				->			Dict:




				'''simple docstring'''






				for model_class_name in self.all_model_classes:
							a__				,       a__				,       a__     				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(_snake_case					,   _snake_case					,   _snake_case					,   _snake_case				)


	def        _lowerCAmelCase				(   self		:      Tuple				)				->			Optional[int]:




				'''simple docstring'''






				for model_class_name in self.all_model_classes:
							a__				,       a__				,       a__     				=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    _snake_case					,   _snake_case					,   _snake_case					,   _snake_case				)


	@slow
	def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Optional[int]:




				'''simple docstring'''






				a__     				=		XGLMTokenizer.from_pretrained('XGLM'					,   padding_side='left'				)
				a__     				=		tokenizer(['Hello this is a long string', 'Hey']					,   return_tensors='np'					,   padding=_snake_case					,   truncation=_snake_case				)

				a__     				=		FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)
				a__     				=		1
				a__     				=		False

				a__     				=		jax.jit(model.generate				)

				a__     				=		jit_generate(inputs['input_ids']					,   attention_mask=inputs['attention_mask']				).sequences

				a__     				=		tokenizer.batch_decode(_snake_case					,   skip_special_tokens=_snake_case				)

				a__     				=		[
				    'Hello this is a long string of questions, but I\'m not sure if I\'m',
				    'Hey, I\'m a newbie to the forum and I\'',
				]

				self.assertListEqual(_snake_case					,   _snake_case				)


	@is_pt_flax_cross_test
	def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Union[str, Any]:




				'''simple docstring'''






				a__				,       a__     				=		self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__				):
										# prepare inputs
										a__     				=		self._prepare_for_class(_snake_case					,   _snake_case				)
										a__     				=		{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

										# load corresponding PyTorch class
										a__     				=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
										a__     				=		getattr(_snake_case					,   _snake_case				)

										a__				,       a__     				=		pt_inputs['input_ids'].shape
										a__     				=		np.random.randint(0					,   seq_length - 1					,   size=(batch_size,)				)
										for batch_idx, start_index in enumerate(_snake_case				):
													a__     				=		0
													a__     				=		1
													a__     				=		0
													a__     				=		1
										a__     				=		pt_model_class(_snake_case				).eval()
										# Flax models don't use the `use_cache` option and cache is not returned as a default.
										# So we disable `use_cache` here for PyTorch model.
										a__     				=		False
										a__     				=		model_class(_snake_case					,   dtype=jnp.floataa				)
										a__     				=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,   _snake_case				)
										a__     				=		fx_state

										with torch.no_grad():
													a__     				=		pt_model(**_snake_case				).to_tuple()

										a__     				=		fx_model(**_snake_case				).to_tuple()
										self.assertEqual(len(_snake_case				)					,   len(_snake_case				)					,   'Output lengths differ between Flax and PyTorch'				)
										for fx_output, pt_output in zip(_snake_case					,   _snake_case				):
													self.assert_almost_equals(fx_output[:, -1]					,   pt_output[:, -1].numpy()					,   4E-2				)

										with tempfile.TemporaryDirectory() as tmpdirname:
													pt_model.save_pretrained(_snake_case				)
													a__     				=		model_class.from_pretrained(_snake_case					,   from_pt=_snake_case				)

										a__     				=		fx_model_loaded(**_snake_case				).to_tuple()
										self.assertEqual(
										    len(_snake_case				)					,   len(_snake_case				)					,   'Output lengths differ between Flax and PyTorch'				)
										for fx_output_loaded, pt_output in zip(_snake_case					,   _snake_case				):
													self.assert_almost_equals(fx_output_loaded[:, -1]					,   pt_output[:, -1].numpy()					,   4E-2				)


	@is_pt_flax_cross_test
	def        _lowerCAmelCase				(   self		:      List[Any]				)				->			Union[str, Any]:




				'''simple docstring'''






				a__				,       a__     				=		self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__				):
										# prepare inputs
										a__     				=		self._prepare_for_class(_snake_case					,   _snake_case				)
										a__     				=		{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

										# load corresponding PyTorch class
										a__     				=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
										a__     				=		getattr(_snake_case					,   _snake_case				)

										a__     				=		pt_model_class(_snake_case				).eval()
										a__     				=		False
										a__     				=		model_class(_snake_case					,   dtype=jnp.floataa				)

										a__     				=		load_flax_weights_in_pytorch_model(_snake_case					,   fx_model.params				)
										a__				,       a__     				=		pt_inputs['input_ids'].shape
										a__     				=		np.random.randint(0					,   seq_length - 1					,   size=(batch_size,)				)
										for batch_idx, start_index in enumerate(_snake_case				):
													a__     				=		0
													a__     				=		1
													a__     				=		0
													a__     				=		1

										# make sure weights are tied in PyTorch
										pt_model.tie_weights()

										with torch.no_grad():
													a__     				=		pt_model(**_snake_case				).to_tuple()

										a__     				=		fx_model(**_snake_case				).to_tuple()
										self.assertEqual(len(_snake_case				)					,   len(_snake_case				)					,   'Output lengths differ between Flax and PyTorch'				)
										for fx_output, pt_output in zip(_snake_case					,   _snake_case				):
													self.assert_almost_equals(fx_output[:, -1]					,   pt_output[:, -1].numpy()					,   4E-2				)

										with tempfile.TemporaryDirectory() as tmpdirname:
													fx_model.save_pretrained(_snake_case				)
													a__     				=		pt_model_class.from_pretrained(_snake_case					,   from_flax=_snake_case				)

										with torch.no_grad():
													a__     				=		pt_model_loaded(**_snake_case				).to_tuple()

										self.assertEqual(
										    len(_snake_case				)					,   len(_snake_case				)					,   'Output lengths differ between Flax and PyTorch'				)
										for fx_output, pt_output in zip(_snake_case					,   _snake_case				):
													self.assert_almost_equals(fx_output[:, -1]					,   pt_output[:, -1].numpy()					,   4E-2				)



	@slow
	def        _lowerCAmelCase				(   self		:      List[str]				)				->			int:




				'''simple docstring'''






				for model_class_name in self.all_model_classes:
							a__     				=		model_class_name.from_pretrained('facebook/xglm-564M'				)
							a__     				=		model(np.ones((1, 1)				)				)
							self.assertIsNotNone(_snake_case				)



"""simple docstring"""



import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand





def   _lowerCamelCase ( UpperCAmelCase__					)							->     Any:


   '''simple docstring'''


   return EnvironmentCommand()




class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      ArgumentParser				)				->			int:




    '''simple docstring'''






    a__     				=		parser.add_parser('env'					,   help='Print relevant system environment info.'				)
    download_parser.set_defaults(func=_snake_case				)


 def        _lowerCAmelCase				(   self		:      List[str]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		{
        '`datasets` version': version,
        'Platform': platform.platform(),
        'Python version': platform.python_version(),
        'Huggingface_hub version': huggingface_hub.__version__,
        'PyArrow version': pyarrow.__version__,
        'Pandas version': pandas.__version__,
    }

    print('\nCopy-and-paste the text below in your GitHub issue.\n'				)
    print(self.format_dict(_snake_case				)				)

    return info



 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      Tuple				)				->			Optional[int]:




    '''simple docstring'''






    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]				) + "\n"


