
'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 	A  (   UpperCamelCase_       :  List[Any]   )       ->							Tuple:







			'''simple docstring'''



			if "img_encoder.pos_embed" in name:
						lowerCAmelCase__										=				name.replace("img_encoder.pos_embed"    ,						"vision_model.embeddings.position_embeddings"   )
			if "img_encoder.patch_embed.proj" in name:
						lowerCAmelCase__										=				name.replace("img_encoder.patch_embed.proj"    ,						"vision_model.embeddings.patch_embeddings.projection"   )
			if "img_encoder.patch_embed.norm" in name:
						lowerCAmelCase__										=				name.replace("img_encoder.patch_embed.norm"    ,						"vision_model.embeddings.layernorm"   )
			if "img_encoder.layers" in name:
						lowerCAmelCase__										=				name.replace("img_encoder.layers"    ,						"vision_model.encoder.stages"   )
			if "blocks" in name and "res" not in name:
						lowerCAmelCase__										=				name.replace("blocks"    ,						"layers"   )
			if "attn" in name and "pre_assign" not in name:
						lowerCAmelCase__										=				name.replace("attn"    ,						"self_attn"   )
			if "proj" in name and "self_attn" in name and "text" not in name:
						lowerCAmelCase__										=				name.replace("proj"    ,						"out_proj"   )
			if "pre_assign_attn.attn.proj" in name:
						lowerCAmelCase__										=				name.replace("pre_assign_attn.attn.proj"    ,						"pre_assign_attn.attn.out_proj"   )
			if "norm1" in name:
						lowerCAmelCase__										=				name.replace("norm1"    ,						"layer_norm1"   )
			if "norm2" in name and "pre_assign" not in name:
						lowerCAmelCase__										=				name.replace("norm2"    ,						"layer_norm2"   )
			if "img_encoder.norm" in name:
						lowerCAmelCase__										=				name.replace("img_encoder.norm"    ,						"vision_model.layernorm"   )
			# text encoder
			if "text_encoder.token_embedding" in name:
						lowerCAmelCase__										=				name.replace("text_encoder.token_embedding"    ,						"text_model.embeddings.token_embedding"   )
			if "text_encoder.positional_embedding" in name:
						lowerCAmelCase__										=				name.replace("text_encoder.positional_embedding"    ,						"text_model.embeddings.position_embedding.weight"   )
			if "text_encoder.transformer.resblocks." in name:
						lowerCAmelCase__										=				name.replace("text_encoder.transformer.resblocks."    ,						"text_model.encoder.layers."   )
			if "ln_1" in name:
						lowerCAmelCase__										=				name.replace("ln_1"    ,						"layer_norm1"   )
			if "ln_2" in name:
						lowerCAmelCase__										=				name.replace("ln_2"    ,						"layer_norm2"   )
			if "c_fc" in name:
						lowerCAmelCase__										=				name.replace("c_fc"    ,						"fc1"   )
			if "c_proj" in name:
						lowerCAmelCase__										=				name.replace("c_proj"    ,						"fc2"   )
			if "text_encoder" in name:
						lowerCAmelCase__										=				name.replace("text_encoder"    ,						"text_model"   )
			if "ln_final" in name:
						lowerCAmelCase__										=				name.replace("ln_final"    ,						"final_layer_norm"   )
			# projection layers
			if "img_projector.linear_hidden." in name:
						lowerCAmelCase__										=				name.replace("img_projector.linear_hidden."    ,						"visual_projection."   )
			if "img_projector.linear_out." in name:
						lowerCAmelCase__										=				name.replace("img_projector.linear_out."    ,						"visual_projection.3."   )
			if "text_projector.linear_hidden" in name:
						lowerCAmelCase__										=				name.replace("text_projector.linear_hidden"    ,						"text_projection"   )
			if "text_projector.linear_out" in name:
						lowerCAmelCase__										=				name.replace("text_projector.linear_out"    ,						"text_projection.3"   )

			return name







def 	A  (   UpperCamelCase_       :  str    ,						UpperCamelCase_       :  str   )       ->							List[Any]:







			'''simple docstring'''



			for key in orig_state_dict.copy().keys():
						lowerCAmelCase__										=				orig_state_dict.pop(UpperCamelCase_   )

						if "qkv" in key:
									# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
									# we need to split them up into separate matrices/vectors
									lowerCAmelCase__										=				key.split("."   )
									lowerCAmelCase__						,lowerCAmelCase__										=				int(key_split[2]   ), int(key_split[4]   )
									lowerCAmelCase__										=				config.vision_config.hidden_size
									if "weight" in key:
												lowerCAmelCase__										=				val[:dim, :]
												lowerCAmelCase__										=				val[dim : dim * 2, :]
												lowerCAmelCase__										=				val[-dim:, :]
									else:
												lowerCAmelCase__										=				val[:dim]
												lowerCAmelCase__										=				val[dim : dim * 2]
												lowerCAmelCase__										=				val[-dim:]
						elif "in_proj" in key:
									# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
									# we need to split them up into separate matrices/vectors
									lowerCAmelCase__										=				key.split("."   )
									lowerCAmelCase__										=				int(key_split[3]   )
									lowerCAmelCase__										=				config.text_config.hidden_size
									if "weight" in key:
												lowerCAmelCase__										=				val[:dim, :]
												lowerCAmelCase__										=				val[
												    dim : dim * 2, :
												]
												lowerCAmelCase__										=				val[-dim:, :]
									else:
												lowerCAmelCase__										=				val[:dim]
												lowerCAmelCase__										=				val[dim : dim * 2]
												lowerCAmelCase__										=				val[-dim:]
						else:
									lowerCAmelCase__										=				rename_key(UpperCamelCase_   )
									# squeeze if necessary
									if (
									    "text_projection.0" in new_name
									    or "text_projection.3" in new_name
									    or "visual_projection.0" in new_name
									    or "visual_projection.3" in new_name
									):
												lowerCAmelCase__										=				val.squeeze_()
									else:
												lowerCAmelCase__										=				val

			return orig_state_dict







def 	A  (   )       ->							Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__										=				"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase__										=				Image.open(requests.get(UpperCamelCase_    ,						stream=UpperCamelCase_   ).raw   )
			return im







@torch.no_grad()
def 	A  (   UpperCamelCase_       :  List[Any]    ,						UpperCamelCase_       :  Any    ,						UpperCamelCase_       :  Tuple="groupvit-gcc-yfcc"    ,						UpperCamelCase_       :  Dict=False   )       ->							Any:







			'''simple docstring'''



			lowerCAmelCase__										=				GroupViTConfig()
			lowerCAmelCase__										=				GroupViTModel(UpperCamelCase_   ).eval()

			lowerCAmelCase__										=				torch.load(UpperCamelCase_    ,						map_location="cpu"   )["model"]
			lowerCAmelCase__										=				convert_state_dict(UpperCamelCase_    ,						UpperCamelCase_   )
			lowerCAmelCase__						,lowerCAmelCase__										=				model.load_state_dict(UpperCamelCase_    ,						strict=UpperCamelCase_   )
			assert missing_keys == ["text_model.embeddings.position_ids"]
			assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCamelCase_   ) == 0)

			# verify result
			lowerCAmelCase__										=				CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"   )
			lowerCAmelCase__										=				prepare_img()
			lowerCAmelCase__										=				processor(text=["a photo of a cat", "a photo of a dog"]    ,						images=UpperCamelCase_    ,						padding=UpperCamelCase_    ,						return_tensors="pt"   )

			with torch.no_grad():
						lowerCAmelCase__										=				model(**UpperCamelCase_   )

			if model_name == "groupvit-gcc-yfcc":
						lowerCAmelCase__										=				torch.tensor([[13.3_523, 6.3_629]]   )
			elif model_name == "groupvit-gcc-redcaps":
						lowerCAmelCase__										=				torch.tensor([[16.1_873, 8.6_230]]   )
			else:
						raise ValueError(F"""Model name {model_name} not supported."""   )
			assert torch.allclose(outputs.logits_per_image    ,						UpperCamelCase_    ,						atol=1E-3   )

			processor.save_pretrained(UpperCamelCase_   )
			model.save_pretrained(UpperCamelCase_   )
			print("Successfully saved processor and model to"    ,						UpperCamelCase_   )

			if push_to_hub:
						print("Pushing to the hub..."   )
						processor.push_to_hub(UpperCamelCase_    ,						organization="nielsr"   )
						model.push_to_hub(UpperCamelCase_    ,						organization="nielsr"   )


if __name__ == "__main__":
		UpperCAmelCase__ :			List[str]											=		argparse.ArgumentParser()
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
		)
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
		parser.add_argument(
		    "--model_name",
		    default="groupvit-gccy-fcc",
		    type=str,
		    help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
		)
		parser.add_argument(
		    "--push_to_hub",
		    action="store_true",
		    help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
		)
		UpperCAmelCase__ :			Any											=		parser.parse_args()

		convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)










"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     snake_case   					=				'''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						)						->				List[Any]:
  if attention_mask is None:
    _snake_case   	=      np.where(input_ids != config.pad_token_id  ,						1  ,						0	)
  if decoder_attention_mask is None:
    _snake_case   	=      np.where(decoder_input_ids != config.pad_token_id  ,						1  ,						0	)
  if head_mask is None:
    _snake_case   	=      np.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  if cross_attn_head_mask is None:
    _snake_case   	=      np.ones((config.decoder_layers, config.decoder_attention_heads)	)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]=1_3							,       __lowerCamelCase    :	int=7							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[str]=False							,       __lowerCamelCase    :	Optional[int]=9_9							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Tuple=2							,       __lowerCamelCase    :	Tuple=4							,       __lowerCamelCase    :	Optional[int]=4							,       __lowerCamelCase    :	Tuple="gelu"							,       __lowerCamelCase    :	int=0.1							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Optional[int]=2							,       __lowerCamelCase    :	Optional[Any]=1							,       __lowerCamelCase    :	Dict=0							,       __lowerCamelCase    :	List[str]=0.0_2							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      seq_length
     _snake_case   	=      is_training
     _snake_case   	=      use_labels
     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_act
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      eos_token_id
     _snake_case   	=      pad_token_id
     _snake_case   	=      bos_token_id
     _snake_case   	=      initializer_range



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,       self.vocab_size				)							,       3							,       self.vocab_size				)
     _snake_case   	=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,       dtype=np.intaa				))							,       -1				)

     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)

     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=self.hidden_size							,       encoder_layers=self.num_hidden_layers							,       decoder_layers=self.num_hidden_layers							,       encoder_attention_heads=self.num_attention_heads							,       decoder_attention_heads=self.num_attention_heads							,       encoder_ffn_dim=self.intermediate_size							,       decoder_ffn_dim=self.intermediate_size							,       dropout=self.hidden_dropout_prob							,       attention_dropout=self.attention_probs_dropout_prob							,       max_position_embeddings=self.max_position_embeddings							,       eos_token_id=self.eos_token_id							,       bos_token_id=self.bos_token_id							,       pad_token_id=self.pad_token_id							,       initializer_range=self.initializer_range							,       use_cache=__lowerCamelCase							,       )
     _snake_case   	=      prepare_blenderbot_inputs_dict(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.prepare_config_and_inputs()
     return config, inputs_dict



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

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

     _snake_case   	=      jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,       )
     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase				)

     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      2_0
     _snake_case   	=      model_class_name(__lowerCamelCase				)

     _snake_case   	=      model.encode(inputs_dict['''input_ids''']				)

     _snake_case	,     _snake_case   	=      (
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

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

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

     _snake_case   	=      model.decode(
         decoder_input_ids[:, :-1]							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       past_key_values=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )
     _snake_case   	=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,       dtype='''i4'''				)
     _snake_case   	=      model.decode(
         decoder_input_ids[:, -1:]							,       __lowerCamelCase							,       past_key_values=outputs_cache.past_key_values							,       decoder_attention_mask=__lowerCamelCase							,       decoder_position_ids=__lowerCamelCase							,       )

     _snake_case   	=      model.decode(__lowerCamelCase							,       __lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase				)

     _snake_case   	=      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     UpperCAmelCase    (	unittest.TestCase  ):
   A__		:	Dict           =  99



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      np.array(
         [
             [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
             [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
             [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
             [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
             [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
             [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
             [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
             [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
             [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
             [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
             [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
             [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
             [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
         ]							,       dtype=np.intaa							,       )

     _snake_case   	=      input_ids.shape[0]
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=2_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=3_2							,       decoder_ffn_dim=3_2							,       max_position_embeddings=4_8							,       eos_token_id=2							,       pad_token_id=1							,       bos_token_id=0							,       )
     return config, input_ids, batch_size



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case	,     _snake_case   	=      self._get_config_and_data()
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase				)
     _snake_case   	=      (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      BlenderbotConfig(
         vocab_size=self.vocab_size							,       d_model=1_4							,       encoder_layers=2							,       decoder_layers=2							,       encoder_attention_heads=2							,       decoder_attention_heads=2							,       encoder_ffn_dim=8							,       decoder_ffn_dim=8							,       max_position_embeddings=4_8							,       )
     _snake_case   	=      FlaxBlenderbotForConditionalGeneration(__lowerCamelCase				)
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,       dtype=np.intaa				)
     _snake_case   	=      lm_model(input_ids=__lowerCamelCase							,       decoder_input_ids=__lowerCamelCase				)
     _snake_case   	=      (*summary.shape, config.vocab_size)
     self.assertEqual(outputs['''logits'''].shape							,       __lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,       dtype=np.intaa				)
     _snake_case   	=      shift_tokens_right(__lowerCamelCase							,       1							,       2				)
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     _snake_case   	=      np.equal(__lowerCamelCase							,       1				).astype(np.floataa				).sum()
     self.assertEqual(shifted.shape							,       input_ids.shape				)
     self.assertEqual(__lowerCamelCase							,       n_pad_before - 1				)
     self.assertTrue(np.equal(shifted[:, 0]							,       2				).all()				)



@require_flax
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase,__SCREAMING_SNAKE_CASE  ):
   A__		:	Union[str, Any]           =  True
   A__		:	int           =  (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   A__		:	List[Any]           =  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      FlaxBlenderbotModelTester(self				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)
         _snake_case   	=      model_class(__lowerCamelCase				)

         @jax.jit
         def encode_jitted(__lowerCamelCase    :	int							,       __lowerCamelCase    :	Optional[int]=None							,       **__lowerCamelCase    :	Union[str, Any]				):
           return model.encode(input_ids=__lowerCamelCase							,       attention_mask=__lowerCamelCase				)

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      encode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
         _snake_case   	=      model_class(__lowerCamelCase				)
         _snake_case   	=      model.encode(inputs_dict['''input_ids''']							,       inputs_dict['''attention_mask''']				)

         _snake_case   	=      {
             '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
             '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
             '''encoder_outputs''': encoder_outputs,
         }

         @jax.jit
         def decode_jitted(__lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):
           return model.decode(
               decoder_input_ids=__lowerCamelCase							,       decoder_attention_mask=__lowerCamelCase							,       encoder_outputs=__lowerCamelCase							,       )

         with self.subTest('''JIT Enabled'''				):
           _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         with self.subTest('''JIT Disabled'''				):
           with jax.disable_jit():
             _snake_case   	=      decode_jitted(**__lowerCamelCase				).to_tuple()

         self.assertEqual(len(__lowerCamelCase				)							,       len(__lowerCamelCase				)				)
         for jitted_output, output in zip(__lowerCamelCase							,       __lowerCamelCase				):
           self.assertEqual(jitted_output.shape							,       output.shape				)



   @slow
   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     for model_class_name in self.all_model_classes:
       _snake_case   	=      model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       _snake_case   	=      np.ones((1, 1)				) * model.config.eos_token_id
       _snake_case   	=      model(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)





   @unittest.skipUnless(jax_device != '''cpu'''							,       '''3B test too slow on CPU.'''				)
   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
     _snake_case   	=      {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

     _snake_case   	=      FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''							,       from_pt=__lowerCamelCase				)
     _snake_case   	=      BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

     _snake_case   	=      ['''Sam''']
     _snake_case   	=      tokenizer(__lowerCamelCase							,       return_tensors='''jax'''				)

     _snake_case   	=      model.generate(**__lowerCamelCase							,       **__lowerCamelCase				)
     _snake_case   	=      '''Sam is a great name. It means "sun" in Gaelic.'''

     _snake_case   	=      tokenizer.batch_decode(__lowerCamelCase							,       **__lowerCamelCase				)
     assert generated_txt[0].strip() == tgt_text





