




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 						A:



 '''simple docstring'''


 def __init__(       self      :						List[Any]		,							A_      :						Tuple		,							A_      :						Union[str, Any]=14		,							A_      :						List[str]=7		,							A_      :						List[str]=True		,							A_      :						Tuple=True		,							A_      :						Optional[Any]=False		,							A_      :						List[Any]=True		,							A_      :						Union[str, Any]=99		,							A_      :						int=32		,							A_      :						Optional[Any]=4		,							A_      :						Any=4		,							A_      :						str=4		,							A_      :						Union[str, Any]=37		,							A_      :						str="gelu"		,							A_      :						Optional[Any]=0.1		,							A_      :						Tuple=0.1		,							A_      :						Any=512		,							A_      :						List[str]=0.02		,							)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					parent
       lowerCamelCase_	  =					batch_size
       lowerCamelCase_	  =					seq_length
       lowerCamelCase_	  =					is_training
       lowerCamelCase_	  =					use_input_mask
       lowerCamelCase_	  =					use_token_type_ids
       lowerCamelCase_	  =					use_labels
       lowerCamelCase_	  =					vocab_size
       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					rotary_dim
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					intermediate_size
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					hidden_dropout_prob
       lowerCamelCase_	  =					attention_probs_dropout_prob
       lowerCamelCase_	  =					max_position_embeddings
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					None
       lowerCamelCase_	  =					vocab_size - 1
       lowerCamelCase_	  =					vocab_size - 1
       lowerCamelCase_	  =					vocab_size - 1

 def        a__				(       self      :						str						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					ids_tensor([self.batch_size, self.seq_length]		,							self.vocab_size						)

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

       lowerCamelCase_	  =					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=snake_case_		,							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        a__				(       self      :						Dict						)     ->			Optional[int]:

       """simple docstring"""






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

 def        a__				(       self      :						Optional[Any]		,							A_      :						Tuple		,							A_      :						Tuple		,							A_      :						Dict		,							A_      :						Optional[int]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					20
       lowerCamelCase_	  =					model_class_name(snake_case_						)

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

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

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

       lowerCamelCase_	  =					model(snake_case_						)

       lowerCamelCase_	  =					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        a__				(       self      :						Optional[int]		,							A_      :						List[str]		,							A_      :						Any		,							A_      :						Optional[int]		,							A_      :						int						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					20
       lowerCamelCase_	  =					model_class_name(snake_case_						)

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

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

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

       lowerCamelCase_	  =					model(snake_case_		,							attention_mask=snake_case_						)

       lowerCamelCase_	  =					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 						A(		_a							,   _a							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 UpperCamelCase		     =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()

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

       """simple docstring"""






       lowerCamelCase_	  =					FlaxGPTJModelTester(self						)

 def        a__				(       self      :						List[str]						)     ->			Tuple:

       """simple docstring"""






       for model_class_name in self.all_model_classes:
             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(snake_case_		,							snake_case_		,							snake_case_		,							snake_case_						)

 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       for model_class_name in self.all_model_classes:
             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					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_						)

 @tooslow
 def        a__				(       self      :						Union[str, Any]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					GPTaTokenizer.from_pretrained('gpt2'		,							pad_token='<|endoftext|>'		,							padding_side='left'						)
       lowerCamelCase_	  =					tokenizer(['Hello this is a long string', 'Hey']		,							return_tensors='np'		,							padding=snake_case_		,							truncation=snake_case_						)

       lowerCamelCase_	  =					FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'						)
       lowerCamelCase_	  =					False
       lowerCamelCase_	  =					model.config.eos_token_id

       lowerCamelCase_	  =					jax.jit(model.generate						)

       lowerCamelCase_	  =					jit_generate(
           inputs['input_ids']		,							attention_mask=inputs['attention_mask']		,							pad_token_id=tokenizer.pad_token_id						).sequences

       lowerCamelCase_	  =					tokenizer.batch_decode(snake_case_		,							skip_special_tokens=snake_case_						)

       lowerCamelCase_	  =					[
           '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(snake_case_		,							snake_case_						)

 @is_pt_flax_cross_test
 def        a__				(       self      :						Optional[Any]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					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
                   lowerCamelCase_	  =					self._prepare_for_class(snake_case_		,							snake_case_						)
                   lowerCamelCase_	  =					{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

                   lowerCamelCase_  ,       lowerCamelCase_	  =					pt_inputs['input_ids'].shape
                   lowerCamelCase_	  =					np.random.randint(0		,							seq_length - 1		,							size=(batch_size,)						)
                   for batch_idx, start_index in enumerate(snake_case_						):
                         lowerCamelCase_	  =					0
                         lowerCamelCase_	  =					1
                         lowerCamelCase_	  =					0
                         lowerCamelCase_	  =					1
                   lowerCamelCase_	  =					pt_model_class(snake_case_						).eval()
                   lowerCamelCase_	  =					model_class(snake_case_		,							dtype=jnp.floataa						)

                   lowerCamelCase_	  =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,							snake_case_						)
                   lowerCamelCase_	  =					fx_state

                   with torch.no_grad():
                         lowerCamelCase_	  =					pt_model(**snake_case_						).to_tuple()

                   lowerCamelCase_	  =					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_						)
                         lowerCamelCase_	  =					model_class.from_pretrained(snake_case_		,							from_pt=snake_case_						)

                   lowerCamelCase_	  =					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        a__				(       self      :						List[str]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					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
                   lowerCamelCase_	  =					self._prepare_for_class(snake_case_		,							snake_case_						)
                   lowerCamelCase_	  =					{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

                   lowerCamelCase_	  =					pt_model_class(snake_case_						).eval()
                   lowerCamelCase_	  =					model_class(snake_case_		,							dtype=jnp.floataa						)

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

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

                   with torch.no_grad():
                         lowerCamelCase_	  =					pt_model(**snake_case_						).to_tuple()

                   lowerCamelCase_	  =					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_						)
                         lowerCamelCase_	  =					pt_model_class.from_pretrained(snake_case_		,							from_flax=snake_case_						)

                   with torch.no_grad():
                         lowerCamelCase_	  =					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						)





 @tooslow
 def        a__				(       self      :						Any						)     ->			Union[str, Any]:

       """simple docstring"""






       for model_class_name in self.all_model_classes:
             lowerCamelCase_	  =					model_class_name.from_pretrained('EleutherAI/gpt-j-6B'						)
             lowerCamelCase_	  =					model(np.ones((1, 1)						)						)
             self.assertIsNotNone(snake_case_						)






import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      return EnvironmentCommand()







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @staticmethod
 def        a__				(       A_      :						ArgumentParser						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					parser.add_parser('env'						)
       download_parser.set_defaults(func=A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					huggingface_hub.__version__

       lowerCamelCase_	  =					'not installed'
       lowerCamelCase_	  =					'NA'
       if is_torch_available():
             import torch

             lowerCamelCase_	  =					torch.__version__
             lowerCamelCase_	  =					torch.cuda.is_available()

       lowerCamelCase_	  =					'not installed'
       if is_transformers_available():
             import transformers

             lowerCamelCase_	  =					transformers.__version__

       lowerCamelCase_	  =					'not installed'
       if is_accelerate_available():
             import accelerate

             lowerCamelCase_	  =					accelerate.__version__

       lowerCamelCase_	  =					'not installed'
       if is_xformers_available():
             import xformers

             lowerCamelCase_	  =					xformers.__version__

       lowerCamelCase_	  =					{
           '`diffusers` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'PyTorch version (GPU?)': f"""{pt_version} ({pt_cuda_available})""",
           'Huggingface_hub version': hub_version,
           'Transformers version': transformers_version,
           'Accelerate version': accelerate_version,
           'xFormers version': xformers_version,
           'Using GPU in script?': '<fill in>',
           'Using distributed or parallel set-up in script?': '<fill in>',
       }

       print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'						)
       print(self.format_dict(A_						)						)

       return info





 @staticmethod
 def        a__				(       A_      :						Dict						)     ->			Any:

       """simple docstring"""






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



