



'''simple docstring'''



import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


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

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
							import torch
class 	lowerCamelCase_							:







						def __init__(  self   :      Optional[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]=14				,    lowerCAmelCase__   :      Optional[Any]=7				,    lowerCAmelCase__   :      Optional[Any]=True				,    lowerCAmelCase__   :      Optional[int]=True				,    lowerCAmelCase__   :      str=False				,    lowerCAmelCase__   :      Any=True				,    lowerCAmelCase__   :      Optional[Any]=99				,    lowerCAmelCase__   :      Optional[Any]=32				,    lowerCAmelCase__   :      Optional[int]=4				,    lowerCAmelCase__   :      List[Any]=4				,    lowerCAmelCase__   :      Optional[int]=4				,    lowerCAmelCase__   :      Dict=37				,    lowerCAmelCase__   :      Union[str, Any]="gelu"				,    lowerCAmelCase__   :      Any=0.1				,    lowerCAmelCase__   :      Optional[int]=0.1				,    lowerCAmelCase__   :      List[Any]=5_12				,    lowerCAmelCase__   :      List[str]=0.02				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			parent
													SCREAMING_SNAKE_CASE					:      Tuple     					=			batch_size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			seq_length
													SCREAMING_SNAKE_CASE					:      Dict     					=			is_training
													SCREAMING_SNAKE_CASE					:      int     					=			use_input_mask
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_token_type_ids
													SCREAMING_SNAKE_CASE					:      Any     					=			use_labels
													SCREAMING_SNAKE_CASE					:      str     					=			vocab_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      Tuple     					=			rotary_dim
													SCREAMING_SNAKE_CASE					:      List[str]     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      Dict     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      Tuple     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			max_position_embeddings
													SCREAMING_SNAKE_CASE					:      Dict     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      str     					=			None
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			vocab_size - 1
													SCREAMING_SNAKE_CASE					:      Dict     					=			vocab_size - 1
													SCREAMING_SNAKE_CASE					:      List[str]     					=			vocab_size - 1







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE					:      Any     					=			random_attention_mask([self.batch_size, self.seq_length]				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			GPTJConfig(
													    vocab_size=self.vocab_size				,    n_embd=self.hidden_size				,    n_layer=self.num_hidden_layers				,    n_head=self.num_attention_heads				,    n_positions=self.max_position_embeddings				,    use_cache=__A				,    bos_token_id=self.bos_token_id				,    eos_token_id=self.eos_token_id				,    pad_token_id=self.pad_token_id				,    rotary_dim=self.rotary_dim				,    )

													return (config, input_ids, input_mask)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Tuple     					=			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
													return config, inputs_dict







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			20
													SCREAMING_SNAKE_CASE					:      Dict     					=			model_class_name(__A				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			model.init_cache(input_ids.shape[0]				,    __A				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			jnp.ones((input_ids.shape[0], max_decoder_length)				,    dtype='''i4'''				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1				)[None, :]				,    (input_ids.shape[0], input_ids.shape[-1] - 1)				)
													SCREAMING_SNAKE_CASE					:      int     					=			model(
													    input_ids[:, :-1]				,    attention_mask=__A				,    past_key_values=__A				,    position_ids=__A				,    )

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

													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(__A				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			20
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model_class_name(__A				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			jnp.concatenate(
													    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]				,    axis=-1				,    )

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.init_cache(input_ids.shape[0]				,    __A				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			jnp.broadcast_to(
													    jnp.arange(input_ids.shape[-1] - 1				)[None, :]				,    (input_ids.shape[0], input_ids.shape[-1] - 1)				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			model(
													    input_ids[:, :-1]				,    attention_mask=__A				,    past_key_values=__A				,    position_ids=__A				,    )
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,    dtype='''i4'''				)
													SCREAMING_SNAKE_CASE					:      int     					=			model(
													    input_ids[:, -1:]				,    past_key_values=outputs_cache.past_key_values				,    attention_mask=__A				,    position_ids=__A				,    )

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(__A				,    attention_mask=__A				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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_flax
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   List[Any]	     =				(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
						_lowerCAmelCase     :   Optional[Any]	     =				(FlaxGPTJForCausalLM,) if is_flax_available() else ()







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			FlaxGPTJModelTester(self				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													for model_class_name in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
																				self.model_tester.check_use_cache_forward(__A				,    __A				,    __A				,    __A				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													for model_class_name in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			self.model_tester.prepare_config_and_inputs()
																				self.model_tester.check_use_cache_forward_with_attn_mask(
																				    __A				,    __A				,    __A				,    __A				)







						@tooslow
						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			GPTaTokenizer.from_pretrained('''gpt2'''				,    pad_token='''<|endoftext|>'''				,    padding_side='''left'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer(['''Hello this is a long string''', '''Hey''']				,    return_tensors='''np'''				,    padding=__A				,    truncation=__A				)

													SCREAMING_SNAKE_CASE					:      int     					=			FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			False
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model.config.eos_token_id

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			jax.jit(model.generate				)

													SCREAMING_SNAKE_CASE					:      int     					=			jit_generate(
													    inputs['''input_ids''']				,    attention_mask=inputs['''attention_mask''']				,    pad_token_id=tokenizer.pad_token_id				).sequences

													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer.batch_decode(__A				,    skip_special_tokens=__A				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			[
													    '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
													    '''Hey, I\'m a little late to the party. I\'m going to''',
													]

													self.assertListEqual(__A				,    __A				)







						@is_pt_flax_cross_test
						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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
																											SCREAMING_SNAKE_CASE					:      Dict     					=			self._prepare_for_class(__A				,    __A				)
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

																											# load corresponding PyTorch class
																											SCREAMING_SNAKE_CASE					:      Any     					=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			getattr(__A				,    __A				)

																											SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			pt_inputs['''input_ids'''].shape
																											SCREAMING_SNAKE_CASE					:      str     					=			np.random.randint(0				,    seq_length - 1				,    size=(batch_size,)				)
																											for batch_idx, start_index in enumerate(__A				):
																																		SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0
																																		SCREAMING_SNAKE_CASE					:      Tuple     					=			1
																																		SCREAMING_SNAKE_CASE					:      Any     					=			0
																																		SCREAMING_SNAKE_CASE					:      Any     					=			1
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			pt_model_class(__A				).eval()
																											SCREAMING_SNAKE_CASE					:      int     					=			model_class(__A				,    dtype=jnp.floataa				)

																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,    __A				)
																											SCREAMING_SNAKE_CASE					:      Any     					=			fx_state

																											with torch.no_grad():
																																		SCREAMING_SNAKE_CASE					:      Optional[int]     					=			pt_model(**__A				).to_tuple()

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

																											with tempfile.TemporaryDirectory() as tmpdirname:
																																		pt_model.save_pretrained(__A				)
																																		SCREAMING_SNAKE_CASE					:      List[str]     					=			model_class.from_pretrained(__A				,    from_pt=__A				)

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







						@is_pt_flax_cross_test
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self._prepare_for_class(__A				,    __A				)
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			{k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

																											# load corresponding PyTorch class
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
																											SCREAMING_SNAKE_CASE					:      str     					=			getattr(__A				,    __A				)

																											SCREAMING_SNAKE_CASE					:      int     					=			pt_model_class(__A				).eval()
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			model_class(__A				,    dtype=jnp.floataa				)

																											SCREAMING_SNAKE_CASE					:      List[str]     					=			load_flax_weights_in_pytorch_model(__A				,    fx_model.params				)
																											SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			pt_inputs['''input_ids'''].shape
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			np.random.randint(0				,    seq_length - 1				,    size=(batch_size,)				)
																											for batch_idx, start_index in enumerate(__A				):
																																		SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0
																																		SCREAMING_SNAKE_CASE					:      Optional[int]     					=			1
																																		SCREAMING_SNAKE_CASE					:      int     					=			0
																																		SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			1

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

																											with torch.no_grad():
																																		SCREAMING_SNAKE_CASE					:      int     					=			pt_model(**__A				).to_tuple()

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

																											with tempfile.TemporaryDirectory() as tmpdirname:
																																		fx_model.save_pretrained(__A				)
																																		SCREAMING_SNAKE_CASE					:      int     					=			pt_model_class.from_pretrained(__A				,    from_flax=__A				)

																											with torch.no_grad():
																																		SCREAMING_SNAKE_CASE					:      Tuple     					=			pt_model_loaded(**__A				).to_tuple()

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







						@tooslow
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													for model_class_name in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      str     					=			model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''				)
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model(np.ones((1, 1)				)				)
																				self.assertIsNotNone(__A				)






'''simple docstring'''



from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id'''					,		['''canonical_dataset_name''', '''org-name/dataset-name''']				)
@pytest.mark.parametrize('''path'''					,		['''filename.csv''', '''filename with blanks.csv''']				)
@pytest.mark.parametrize('''revision'''					,		[None, '''v2''']				)
def 					UpperCAmelCase   (						A  :				int					,		A  :				Tuple					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      int     					=			hf_hub_url(repo_id=A					,		path=A					,		revision=A				)
							assert url == F"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(A				)}"""




