




import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
  import optax
  from flax.training.common_utils import onehot

  from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
  from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			_UpperCamelCase      (				unittest.TestCase		):
      @slow
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			List[Any]:
       UpperCAmelCase__							 =						FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"      )
       UpperCAmelCase__							 =						AutoTokenizer.from_pretrained("google/mt5-small"      )

       UpperCAmelCase__							 =						tokenizer("Hello there" ,      return_tensors="np"      ).input_ids
       UpperCAmelCase__							 =						tokenizer("Hi I am" ,      return_tensors="np"      ).input_ids

       UpperCAmelCase__							 =						shift_tokens_right(lowerCamelCase ,      model.config.pad_token_id ,      model.config.decoder_start_token_id      )

       UpperCAmelCase__							 =						model(lowerCamelCase ,      decoder_input_ids=lowerCamelCase      ).logits
       UpperCAmelCase__							 =						optax.softmax_cross_entropy(lowerCamelCase ,      onehot(lowerCamelCase ,      logits.shape[-1]      )      ).mean()

       UpperCAmelCase__							 =						-(labels.shape[-1] * loss.item())

       UpperCAmelCase__							 =						-84.91_27
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





