



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__:







    def __init__(   self				:						Tuple		,    __snake_case				:						Dict		,    __snake_case				:						int=14		,    __snake_case				:						int=7		,    __snake_case				:						Union[str, Any]=True		,    __snake_case				:						str=True		,    __snake_case				:						List[str]=False		,    __snake_case				:						List[str]=True		,    __snake_case				:						Optional[int]=99		,    __snake_case				:						str=32		,    __snake_case				:						Optional[Any]=4		,    __snake_case				:						Any=4		,    __snake_case				:						Union[str, Any]=4		,    __snake_case				:						List[str]=37		,    __snake_case				:						List[Any]="gelu"		,    __snake_case				:						Optional[Any]=0.1		,    __snake_case				:						List[Any]=0.1		,    __snake_case				:						int=5_12		,    __snake_case				:						List[Any]=0.02		,    ):
      a		:      Tuple       =  parent
      a		:      Any       =  batch_size
      a		:      List[Any]       =  seq_length
      a		:      Optional[int]       =  is_training
      a		:      Dict       =  use_input_mask
      a		:      List[str]       =  use_token_type_ids
      a		:      List[str]       =  use_labels
      a		:      Tuple       =  vocab_size
      a		:      List[Any]       =  hidden_size
      a		:      List[str]       =  rotary_dim
      a		:      Optional[int]       =  num_hidden_layers
      a		:      int       =  num_attention_heads
      a		:      Any       =  intermediate_size
      a		:      Optional[Any]       =  hidden_act
      a		:      Any       =  hidden_dropout_prob
      a		:      Dict       =  attention_probs_dropout_prob
      a		:      List[str]       =  max_position_embeddings
      a		:      Optional[int]       =  initializer_range
      a		:      List[str]       =  None
      a		:      Tuple       =  vocab_size - 1
      a		:      List[str]       =  vocab_size - 1
      a		:      List[str]       =  vocab_size - 1







    def     lowercase_ (   self				:						Any			):
      a		:      Tuple       =  ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

      a		:      Tuple       =  None
      if self.use_input_mask:
        a		:      Optional[Any]       =  random_attention_mask([self.batch_size, self.seq_length]			)

      a		:      Optional[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=__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     lowercase_ (   self				:						Tuple			):
      a		:      Tuple       =  self.prepare_config_and_inputs()
      a		:      Dict       =  config_and_inputs
      a		:      Union[str, Any]       =  {'input_ids': input_ids, 'attention_mask': attention_mask}
      return config, inputs_dict







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						List[str]		,    __snake_case				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						Tuple			):
      a		:      str       =  20
      a		:      Any       =  model_class_name(__snake_case			)

      a		:      Dict       =  model.init_cache(input_ids.shape[0]		,    __snake_case			)
      a		:      Union[str, Any]       =  jnp.ones((input_ids.shape[0], max_decoder_length)		,    dtype='i4'			)

      a		:      Optional[int]       =  jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1			)[None, :]		,    (input_ids.shape[0], input_ids.shape[-1] - 1)			)
      a		:      Dict       =  model(
          input_ids[:, :-1]		,    attention_mask=__snake_case		,    past_key_values=__snake_case		,    position_ids=__snake_case		,    )

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

      a		:      List[Any]       =  model(__snake_case			)

      a		:      str       =  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				:						List[str]		,    __snake_case				:						Dict		,    __snake_case				:						str		,    __snake_case				:						Optional[int]		,    __snake_case				:						Optional[Any]			):
      a		:      Optional[Any]       =  20
      a		:      str       =  model_class_name(__snake_case			)

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

      a		:      int       =  model.init_cache(input_ids.shape[0]		,    __snake_case			)
      a		:      Any       =  jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1			)[None, :]		,    (input_ids.shape[0], input_ids.shape[-1] - 1)			)

      a		:      Optional[Any]       =  model(
          input_ids[:, :-1]		,    attention_mask=__snake_case		,    past_key_values=__snake_case		,    position_ids=__snake_case		,    )
      a		:      List[str]       =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,    dtype='i4'			)
      a		:      Tuple       =  model(
          input_ids[:, -1:]		,    past_key_values=outputs_cache.past_key_values		,    attention_mask=__snake_case		,    position_ids=__snake_case		,    )

      a		:      List[Any]       =  model(__snake_case		,    attention_mask=__snake_case			)

      a		:      List[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 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    lowercase__				=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()







    def     lowercase_ (   self				:						List[Any]			):
      a		:      int       =  FlaxGPTJModelTester(self			)







    def     lowercase_ (   self				:						Any			):
      for model_class_name in self.all_model_classes:
        a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case			)







    def     lowercase_ (   self				:						Optional[int]			):
      for model_class_name in self.all_model_classes:
        a		:      Union[str, Any]       =  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     lowercase_ (   self				:						Any			):
      a		:      str       =  GPTaTokenizer.from_pretrained('gpt2'		,    pad_token='<|endoftext|>'		,    padding_side='left'			)
      a		:      List[str]       =  tokenizer(['Hello this is a long string', 'Hey']		,    return_tensors='np'		,    padding=__snake_case		,    truncation=__snake_case			)

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

      a		:      Tuple       =  jax.jit(model.generate			)

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

      a		:      Dict       =  tokenizer.batch_decode(__snake_case		,    skip_special_tokens=__snake_case			)

      a		:      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(__snake_case		,    __snake_case			)







    @is_pt_flax_cross_test
    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      int       =  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		:      int       =  self._prepare_for_class(__snake_case		,    __snake_case			)
          a		:      Optional[Any]       =  {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

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

          a		:      int       =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,    __snake_case			)
          a		:      str       =  fx_state

          with torch.no_grad():
            a		:      Optional[int]       =  pt_model(**__snake_case			).to_tuple()

          a		:      int       =  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		:      Any       =  model_class.from_pretrained(__snake_case		,    from_pt=__snake_case			)

          a		:      int       =  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     lowercase_ (   self				:						List[str]			):
      a		:      str       =  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		:      str       =  self._prepare_for_class(__snake_case		,    __snake_case			)
          a		:      List[Any]       =  {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          a		:      Union[str, Any]       =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
          a		:      Tuple       =  getattr(__snake_case		,    __snake_case			)

          a		:      str       =  pt_model_class(__snake_case			).eval()
          a		:      int       =  model_class(__snake_case		,    dtype=jnp.floataa			)

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

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

          with torch.no_grad():
            a		:      str       =  pt_model(**__snake_case			).to_tuple()

          a		:      Optional[Any]       =  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		:      str       =  pt_model_class.from_pretrained(__snake_case		,    from_flax=__snake_case			)

          with torch.no_grad():
            a		:      List[str]       =  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     lowercase_ (   self				:						Optional[int]			):
      for model_class_name in self.all_model_classes:
        a		:      Union[str, Any]       =  model_class_name.from_pretrained('EleutherAI/gpt-j-6B'			)
        a		:      Tuple       =  model(np.ones((1, 1)			)			)
        self.assertIsNotNone(__snake_case			)




'''simple docstring'''






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    lowerCamelCase__ (		_A       ):
  return EnvironmentCommand()

class 							a__(      lowerCamelCase__			):







    @staticmethod
    def     lowercase_ (   __snake_case				:						ArgumentParser			):
      a		:      Tuple       =  parser.add_parser('env'			)
      download_parser.set_defaults(func=__snake_case			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      str       =  huggingface_hub.__version__

      a		:      List[str]       =  'not installed'
      a		:      List[str]       =  'NA'
      if is_torch_available():
        import torch

        a		:      Optional[Any]       =  torch.__version__
        a		:      int       =  torch.cuda.is_available()

      a		:      Optional[int]       =  'not installed'
      if is_transformers_available():
        import transformers

        a		:      Tuple       =  transformers.__version__

      a		:      Dict       =  'not installed'
      if is_accelerate_available():
        import accelerate

        a		:      int       =  accelerate.__version__

      a		:      Any       =  'not installed'
      if is_xformers_available():
        import xformers

        a		:      Optional[int]       =  xformers.__version__

      a		:      List[str]       =  {
          '`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(__snake_case			)			)

      return info







    @staticmethod
    def     lowercase_ (   __snake_case				:						Union[str, Any]			):
      return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]			) + "\n"